diff --git a/ASSESSMENT.ipynb b/ASSESSMENT.ipynb
new file mode 100644
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+++ b/ASSESSMENT.ipynb
@@ -0,0 +1,2729 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dc1eda95",
+   "metadata": {},
+   "source": [
+    " METHODS IN NUMPY\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "56a12866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "84d4f8fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0.],\n",
+       "       [0., 0., 0.],\n",
+       "       [0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#zeros\n",
+    "np.zeros((3,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3204dba3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1., 1.]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#ones\n",
+    "np.ones((1,5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1931d6d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#arange\n",
+    "np.arange(1,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7f72ed8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.  , 2.25, 2.5 , 2.75, 3.  ])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#linspace()\n",
+    "np.linspace(2.0, 3.0, num=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "230c7125",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1],\n",
+       "       [2, 3],\n",
+       "       [4, 5]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#reshape()\n",
+    "a = np.arange(6).reshape((3, 2))\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b6a81a45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.        , 0.5       , 0.70710678, 0.8660254 , 1.        ])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sin()\n",
+    "np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180. )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f364f0ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.        , 2.71828183, 7.3890561 ])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#exp()\n",
+    "B = np.arange(3)\n",
+    "np.exp(B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "53fc41ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#min()\n",
+    "a = [10,30,26,90,19]\n",
+    "np.min(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "8b1bdc24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#max()\n",
+    "np.max(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "64015bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  2,  5,  7],\n",
+       "       [ 7,  9, 10,  6]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#vstack\n",
+    "a = [[1,2,5,7], [7,9,10,6]]\n",
+    "np.vstack(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "04c98215",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  2,  5,  7,  7,  9, 10,  6])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#hstack\n",
+    "np.hstack(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "792a976f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[45  2  3  4  6]\n",
+      "[1 2 3 4 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#copy()\n",
+    "arr = np.array([1,2,3,4,6])\n",
+    "x = arr.copy()\n",
+    "arr[0] = 45\n",
+    "print(arr)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "b8c7251f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[45 26  3  4  6]\n",
+      "[45 26  3  4  6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#view()\n",
+    "y= arr.view()\n",
+    "arr[1]= 26\n",
+    "print(arr)\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "34389cce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n",
+      "[ 3  5  7  9 11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([3,5,7,9,11])\n",
+    "\n",
+    "x = arr.copy()\n",
+    "y = arr.view()\n",
+    "\n",
+    "print(x.base)\n",
+    "print(y.base)               #base() to check if the copy owns the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "44fbd9a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[10 30 50  6  5  4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#concatenate\n",
+    "arr1 = np.array([10,30,50])\n",
+    "arr2 = ([6,5,4])\n",
+    "\n",
+    "arr = np.concatenate((arr1, arr2))\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "6c693e1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  2   5   8 100]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#sort\n",
+    "arr = np.array([2,8,5,100])\n",
+    "print(np.sort(arr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "04432d13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([1, 3]), array([5, 7]), array([ 9, 11])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#array_split()\n",
+    "data = np.array([1,3,5,7,9,11])\n",
+    "newdata = np.array_split(data, 3)\n",
+    "print(newdata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "6c306807",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([3, 5, 6], dtype=int64),)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#where()\n",
+    "arr = np.array([1,2,3,4,5,4,4])\n",
+    "x = np.where(arr ==4)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "8e5a2cf5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "#searchsorted()\n",
+    "arr = np.array([6,7,8,9])\n",
+    "x = np.searchsorted(arr, 7)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "ee032f6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[7 5 7 7 7]\n",
+      " [5 7 7 5 7]\n",
+      " [7 3 3 7 5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#choice()\n",
+    "from numpy import random\n",
+    "x = random.choice([3,5,7,9], p=[0.1, 0.3, 0.6, 0.0], size =(3,5))\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "73ecc1f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "#astype()\n",
+    "arr = np.array([1.1, 2.1, 3.1])\n",
+    "newarr =arr.astype('i')\n",
+    "print(newarr)\n",
+    "print(newarr.dtype)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "552316b2",
+   "metadata": {},
+   "source": [
+    "### METHODS IN PANDAS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "980e52d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "ee71935e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fileName = 'WorldHappiness_Corruption.csv'\n",
+    "filePath = 'C:\\Kaggle dataset'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "bf0281f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read_csv()\n",
+    "filePathDir = filePath + '/' + fileName\n",
+    "df = pd.read_csv(filePathDir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "fc2fdeb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Norway</td>\n",
+       "      <td>7.5370</td>\n",
+       "      <td>1.616463</td>\n",
+       "      <td>1.533524</td>\n",
+       "      <td>0.796667</td>\n",
+       "      <td>0.635423</td>\n",
+       "      <td>0.362012</td>\n",
+       "      <td>0.315964</td>\n",
+       "      <td>2.277027</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>7.5220</td>\n",
+       "      <td>1.482383</td>\n",
+       "      <td>1.551122</td>\n",
+       "      <td>0.792566</td>\n",
+       "      <td>0.626007</td>\n",
+       "      <td>0.355280</td>\n",
+       "      <td>0.400770</td>\n",
+       "      <td>2.313707</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>7.5040</td>\n",
+       "      <td>1.480633</td>\n",
+       "      <td>1.610574</td>\n",
+       "      <td>0.833552</td>\n",
+       "      <td>0.627163</td>\n",
+       "      <td>0.475540</td>\n",
+       "      <td>0.153527</td>\n",
+       "      <td>2.322715</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>7.4940</td>\n",
+       "      <td>1.564980</td>\n",
+       "      <td>1.516912</td>\n",
+       "      <td>0.858131</td>\n",
+       "      <td>0.620071</td>\n",
+       "      <td>0.290549</td>\n",
+       "      <td>0.367007</td>\n",
+       "      <td>2.276716</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Finland</td>\n",
+       "      <td>7.4690</td>\n",
+       "      <td>1.443572</td>\n",
+       "      <td>1.540247</td>\n",
+       "      <td>0.809158</td>\n",
+       "      <td>0.617951</td>\n",
+       "      <td>0.245483</td>\n",
+       "      <td>0.382612</td>\n",
+       "      <td>2.430182</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>Botswana</td>\n",
+       "      <td>3.4789</td>\n",
+       "      <td>0.997549</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.494102</td>\n",
+       "      <td>0.509089</td>\n",
+       "      <td>0.033407</td>\n",
+       "      <td>0.101786</td>\n",
+       "      <td>0.257241</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.085695</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>788</th>\n",
+       "      <td>Tanzania</td>\n",
+       "      <td>3.4762</td>\n",
+       "      <td>0.457163</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.442678</td>\n",
+       "      <td>0.509343</td>\n",
+       "      <td>0.271541</td>\n",
+       "      <td>0.203881</td>\n",
+       "      <td>0.718963</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.872675</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>789</th>\n",
+       "      <td>Rwanda</td>\n",
+       "      <td>3.3123</td>\n",
+       "      <td>0.343243</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.572383</td>\n",
+       "      <td>0.604088</td>\n",
+       "      <td>0.235705</td>\n",
+       "      <td>0.485542</td>\n",
+       "      <td>0.548445</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.522876</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>3.2992</td>\n",
+       "      <td>0.425564</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.375038</td>\n",
+       "      <td>0.377405</td>\n",
+       "      <td>0.151349</td>\n",
+       "      <td>0.080929</td>\n",
+       "      <td>0.841031</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.047835</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>791</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2.5669</td>\n",
+       "      <td>0.300706</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.266052</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.135235</td>\n",
+       "      <td>0.001226</td>\n",
+       "      <td>1.507236</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.356434</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>792 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Country  happiness_score  gdp_per_capita    family    health  \\\n",
+       "0         Norway           7.5370        1.616463  1.533524  0.796667   \n",
+       "1        Denmark           7.5220        1.482383  1.551122  0.792566   \n",
+       "2        Iceland           7.5040        1.480633  1.610574  0.833552   \n",
+       "3    Switzerland           7.4940        1.564980  1.516912  0.858131   \n",
+       "4        Finland           7.4690        1.443572  1.540247  0.809158   \n",
+       "..           ...              ...             ...       ...       ...   \n",
+       "787     Botswana           3.4789        0.997549  0.000000  0.494102   \n",
+       "788     Tanzania           3.4762        0.457163  0.000000  0.442678   \n",
+       "789       Rwanda           3.3123        0.343243  0.000000  0.572383   \n",
+       "790     Zimbabwe           3.2992        0.425564  0.000000  0.375038   \n",
+       "791  Afghanistan           2.5669        0.300706  0.000000  0.266052   \n",
+       "\n",
+       "      freedom  generosity  government_trust  dystopia_residual continent  \\\n",
+       "0    0.635423    0.362012          0.315964           2.277027    Europe   \n",
+       "1    0.626007    0.355280          0.400770           2.313707    Europe   \n",
+       "2    0.627163    0.475540          0.153527           2.322715    Europe   \n",
+       "3    0.620071    0.290549          0.367007           2.276716    Europe   \n",
+       "4    0.617951    0.245483          0.382612           2.430182    Europe   \n",
+       "..        ...         ...               ...                ...       ...   \n",
+       "787  0.509089    0.033407          0.101786           0.257241    Africa   \n",
+       "788  0.509343    0.271541          0.203881           0.718963    Africa   \n",
+       "789  0.604088    0.235705          0.485542           0.548445    Africa   \n",
+       "790  0.377405    0.151349          0.080929           0.841031    Africa   \n",
+       "791  0.000000    0.135235          0.001226           1.507236      Asia   \n",
+       "\n",
+       "     Year  social_support  cpi_score  \n",
+       "0    2015        0.000000         88  \n",
+       "1    2015        0.000000         91  \n",
+       "2    2015        0.000000         79  \n",
+       "3    2015        0.000000         86  \n",
+       "4    2015        0.000000         90  \n",
+       "..    ...             ...        ...  \n",
+       "787  2020        1.085695         60  \n",
+       "788  2020        0.872675         38  \n",
+       "789  2020        0.522876         54  \n",
+       "790  2020        1.047835         24  \n",
+       "791  2020        0.356434         19  \n",
+       "\n",
+       "[792 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "f17fadf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Norway</td>\n",
+       "      <td>7.537</td>\n",
+       "      <td>1.616463</td>\n",
+       "      <td>1.533524</td>\n",
+       "      <td>0.796667</td>\n",
+       "      <td>0.635423</td>\n",
+       "      <td>0.362012</td>\n",
+       "      <td>0.315964</td>\n",
+       "      <td>2.277027</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>7.522</td>\n",
+       "      <td>1.482383</td>\n",
+       "      <td>1.551122</td>\n",
+       "      <td>0.792566</td>\n",
+       "      <td>0.626007</td>\n",
+       "      <td>0.355280</td>\n",
+       "      <td>0.400770</td>\n",
+       "      <td>2.313707</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>7.504</td>\n",
+       "      <td>1.480633</td>\n",
+       "      <td>1.610574</td>\n",
+       "      <td>0.833552</td>\n",
+       "      <td>0.627163</td>\n",
+       "      <td>0.475540</td>\n",
+       "      <td>0.153527</td>\n",
+       "      <td>2.322715</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>7.494</td>\n",
+       "      <td>1.564980</td>\n",
+       "      <td>1.516912</td>\n",
+       "      <td>0.858131</td>\n",
+       "      <td>0.620071</td>\n",
+       "      <td>0.290549</td>\n",
+       "      <td>0.367007</td>\n",
+       "      <td>2.276716</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Finland</td>\n",
+       "      <td>7.469</td>\n",
+       "      <td>1.443572</td>\n",
+       "      <td>1.540247</td>\n",
+       "      <td>0.809158</td>\n",
+       "      <td>0.617951</td>\n",
+       "      <td>0.245483</td>\n",
+       "      <td>0.382612</td>\n",
+       "      <td>2.430182</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Country  happiness_score  gdp_per_capita    family    health   freedom  \\\n",
+       "0       Norway            7.537        1.616463  1.533524  0.796667  0.635423   \n",
+       "1      Denmark            7.522        1.482383  1.551122  0.792566  0.626007   \n",
+       "2      Iceland            7.504        1.480633  1.610574  0.833552  0.627163   \n",
+       "3  Switzerland            7.494        1.564980  1.516912  0.858131  0.620071   \n",
+       "4      Finland            7.469        1.443572  1.540247  0.809158  0.617951   \n",
+       "\n",
+       "   generosity  government_trust  dystopia_residual continent  Year  \\\n",
+       "0    0.362012          0.315964           2.277027    Europe  2015   \n",
+       "1    0.355280          0.400770           2.313707    Europe  2015   \n",
+       "2    0.475540          0.153527           2.322715    Europe  2015   \n",
+       "3    0.290549          0.367007           2.276716    Europe  2015   \n",
+       "4    0.245483          0.382612           2.430182    Europe  2015   \n",
+       "\n",
+       "   social_support  cpi_score  \n",
+       "0             0.0         88  \n",
+       "1             0.0         91  \n",
+       "2             0.0         79  \n",
+       "3             0.0         86  \n",
+       "4             0.0         90  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#head\n",
+    "filePathDir = filePath + '/' + fileName\n",
+    "df = pd.read_csv(filePathDir)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "aa1f2c5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of          Country  happiness_score  gdp_per_capita    family    health  \\\n",
+       "0         Norway           7.5370        1.616463  1.533524  0.796667   \n",
+       "1        Denmark           7.5220        1.482383  1.551122  0.792566   \n",
+       "2        Iceland           7.5040        1.480633  1.610574  0.833552   \n",
+       "3    Switzerland           7.4940        1.564980  1.516912  0.858131   \n",
+       "4        Finland           7.4690        1.443572  1.540247  0.809158   \n",
+       "..           ...              ...             ...       ...       ...   \n",
+       "787     Botswana           3.4789        0.997549  0.000000  0.494102   \n",
+       "788     Tanzania           3.4762        0.457163  0.000000  0.442678   \n",
+       "789       Rwanda           3.3123        0.343243  0.000000  0.572383   \n",
+       "790     Zimbabwe           3.2992        0.425564  0.000000  0.375038   \n",
+       "791  Afghanistan           2.5669        0.300706  0.000000  0.266052   \n",
+       "\n",
+       "      freedom  generosity  government_trust  dystopia_residual continent  \\\n",
+       "0    0.635423    0.362012          0.315964           2.277027    Europe   \n",
+       "1    0.626007    0.355280          0.400770           2.313707    Europe   \n",
+       "2    0.627163    0.475540          0.153527           2.322715    Europe   \n",
+       "3    0.620071    0.290549          0.367007           2.276716    Europe   \n",
+       "4    0.617951    0.245483          0.382612           2.430182    Europe   \n",
+       "..        ...         ...               ...                ...       ...   \n",
+       "787  0.509089    0.033407          0.101786           0.257241    Africa   \n",
+       "788  0.509343    0.271541          0.203881           0.718963    Africa   \n",
+       "789  0.604088    0.235705          0.485542           0.548445    Africa   \n",
+       "790  0.377405    0.151349          0.080929           0.841031    Africa   \n",
+       "791  0.000000    0.135235          0.001226           1.507236      Asia   \n",
+       "\n",
+       "     Year  social_support  cpi_score  \n",
+       "0    2015        0.000000         88  \n",
+       "1    2015        0.000000         91  \n",
+       "2    2015        0.000000         79  \n",
+       "3    2015        0.000000         86  \n",
+       "4    2015        0.000000         90  \n",
+       "..    ...             ...        ...  \n",
+       "787  2020        1.085695         60  \n",
+       "788  2020        0.872675         38  \n",
+       "789  2020        0.522876         54  \n",
+       "790  2020        1.047835         24  \n",
+       "791  2020        0.356434         19  \n",
+       "\n",
+       "[792 rows x 13 columns]>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#describe\n",
+    "df.describe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "af31bb9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>happiness_score</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>5.473310</td>\n",
+       "      <td>1.124726</td>\n",
+       "      <td>2.5669</td>\n",
+       "      <td>4.590750</td>\n",
+       "      <td>5.485500</td>\n",
+       "      <td>6.300500</td>\n",
+       "      <td>7.808700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>0.929217</td>\n",
+       "      <td>0.385774</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.644242</td>\n",
+       "      <td>0.994544</td>\n",
+       "      <td>1.228677</td>\n",
+       "      <td>2.096000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>family</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>0.504998</td>\n",
+       "      <td>0.552736</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.040410</td>\n",
+       "      <td>1.610574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>health</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>0.648691</td>\n",
+       "      <td>0.232261</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.510070</td>\n",
+       "      <td>0.685397</td>\n",
+       "      <td>0.815641</td>\n",
+       "      <td>1.141000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freedom</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>0.427016</td>\n",
+       "      <td>0.148003</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.325433</td>\n",
+       "      <td>0.439621</td>\n",
+       "      <td>0.546280</td>\n",
+       "      <td>0.724000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>generosity</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>0.212440</td>\n",
+       "      <td>0.121660</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.125784</td>\n",
+       "      <td>0.197000</td>\n",
+       "      <td>0.273250</td>\n",
+       "      <td>0.838075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>government_trust</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>0.125720</td>\n",
+       "      <td>0.109032</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.052862</td>\n",
+       "      <td>0.089000</td>\n",
+       "      <td>0.154250</td>\n",
+       "      <td>0.551910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>1.379277</td>\n",
+       "      <td>1.075276</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.731948</td>\n",
+       "      <td>2.236894</td>\n",
+       "      <td>3.602140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>2017.500000</td>\n",
+       "      <td>1.708904</td>\n",
+       "      <td>2015.0000</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>2017.500000</td>\n",
+       "      <td>2019.000000</td>\n",
+       "      <td>2020.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>social_support</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>0.609302</td>\n",
+       "      <td>0.640754</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.176214</td>\n",
+       "      <td>1.268250</td>\n",
+       "      <td>1.644000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cpi_score</th>\n",
+       "      <td>792.0</td>\n",
+       "      <td>44.327020</td>\n",
+       "      <td>19.508833</td>\n",
+       "      <td>11.0000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>91.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count         mean        std        min          25%  \\\n",
+       "happiness_score    792.0     5.473310   1.124726     2.5669     4.590750   \n",
+       "gdp_per_capita     792.0     0.929217   0.385774     0.0000     0.644242   \n",
+       "family             792.0     0.504998   0.552736     0.0000     0.000000   \n",
+       "health             792.0     0.648691   0.232261     0.0000     0.510070   \n",
+       "freedom            792.0     0.427016   0.148003     0.0000     0.325433   \n",
+       "generosity         792.0     0.212440   0.121660     0.0000     0.125784   \n",
+       "government_trust   792.0     0.125720   0.109032     0.0000     0.052862   \n",
+       "dystopia_residual  792.0     1.379277   1.075276     0.0000     0.000000   \n",
+       "Year               792.0  2017.500000   1.708904  2015.0000  2016.000000   \n",
+       "social_support     792.0     0.609302   0.640754     0.0000     0.000000   \n",
+       "cpi_score          792.0    44.327020  19.508833    11.0000    30.000000   \n",
+       "\n",
+       "                           50%          75%          max  \n",
+       "happiness_score       5.485500     6.300500     7.808700  \n",
+       "gdp_per_capita        0.994544     1.228677     2.096000  \n",
+       "family                0.000000     1.040410     1.610574  \n",
+       "health                0.685397     0.815641     1.141000  \n",
+       "freedom               0.439621     0.546280     0.724000  \n",
+       "generosity            0.197000     0.273250     0.838075  \n",
+       "government_trust      0.089000     0.154250     0.551910  \n",
+       "dystopia_residual     1.731948     2.236894     3.602140  \n",
+       "Year               2017.500000  2019.000000  2020.000000  \n",
+       "social_support        0.176214     1.268250     1.644000  \n",
+       "cpi_score            38.000000    57.000000    91.000000  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Transpose\n",
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "4268deb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index                 128\n",
+       "Country              6336\n",
+       "happiness_score      6336\n",
+       "gdp_per_capita       6336\n",
+       "family               6336\n",
+       "health               6336\n",
+       "freedom              6336\n",
+       "generosity           6336\n",
+       "government_trust     6336\n",
+       "dystopia_residual    6336\n",
+       "continent            6336\n",
+       "Year                 6336\n",
+       "social_support       6336\n",
+       "cpi_score            6336\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#memory_usage\n",
+    "df.memory_usage()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "b54922d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 792 entries, 0 to 791\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Country            792 non-null    object \n",
+      " 1   happiness_score    792 non-null    float64\n",
+      " 2   gdp_per_capita     792 non-null    float64\n",
+      " 3   family             792 non-null    float64\n",
+      " 4   health             792 non-null    float64\n",
+      " 5   freedom            792 non-null    float64\n",
+      " 6   generosity         792 non-null    float64\n",
+      " 7   government_trust   792 non-null    float64\n",
+      " 8   dystopia_residual  792 non-null    float64\n",
+      " 9   continent          792 non-null    object \n",
+      " 10  Year               792 non-null    int64  \n",
+      " 11  social_support     792 non-null    float64\n",
+      " 12  cpi_score          792 non-null    int64  \n",
+      "dtypes: float64(9), int64(2), object(2)\n",
+      "memory usage: 80.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#info()\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "c42e6411",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 792 entries, 0 to 791\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Country            792 non-null    object \n",
+      " 1   happiness_score    792 non-null    float64\n",
+      " 2   gdp_per_capita     792 non-null    float64\n",
+      " 3   family             792 non-null    float64\n",
+      " 4   health             792 non-null    float64\n",
+      " 5   freedom            792 non-null    float64\n",
+      " 6   generosity         792 non-null    float64\n",
+      " 7   government_trust   792 non-null    float64\n",
+      " 8   dystopia_residual  792 non-null    float64\n",
+      " 9   continent          792 non-null    object \n",
+      " 10  Year               792 non-null    int64  \n",
+      " 11  social_support     792 non-null    int32  \n",
+      " 12  cpi_score          792 non-null    int64  \n",
+      "dtypes: float64(8), int32(1), int64(2), object(2)\n",
+      "memory usage: 77.5+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "#astype\n",
+    "df[\"social_support\"] =df['social_support'].astype(int)\n",
+    "print(df.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "3d4c64ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Norway</td>\n",
+       "      <td>7.537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>7.522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>7.504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>7.494</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Country  happiness_score\n",
+       "0       Norway            7.537\n",
+       "1      Denmark            7.522\n",
+       "2      Iceland            7.504\n",
+       "3  Switzerland            7.494"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#loc\n",
+    "df.loc[0:3, ['Country', 'happiness_score']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "cd19571f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   2015-02-04\n",
+       "1   2016-03-05\n",
+       "dtype: datetime64[ns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#to_datetime\n",
+    "df1 = pd.DataFrame({'year':[2015,2016], 'month': [2,3], 'day': [4,5]})\n",
+    "pd.to_datetime(df1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "29ac3169",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2015    132\n",
+       "2016    132\n",
+       "2017    132\n",
+       "2018    132\n",
+       "2019    132\n",
+       "2020    132\n",
+       "Name: Year, dtype: int64"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#value_counts()\n",
+    "df['Year'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "684f8451",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Norway</td>\n",
+       "      <td>7.5370</td>\n",
+       "      <td>1.616463</td>\n",
+       "      <td>1.533524</td>\n",
+       "      <td>0.796667</td>\n",
+       "      <td>0.635423</td>\n",
+       "      <td>0.362012</td>\n",
+       "      <td>0.315964</td>\n",
+       "      <td>2.277027</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>7.5220</td>\n",
+       "      <td>1.482383</td>\n",
+       "      <td>1.551122</td>\n",
+       "      <td>0.792566</td>\n",
+       "      <td>0.626007</td>\n",
+       "      <td>0.355280</td>\n",
+       "      <td>0.400770</td>\n",
+       "      <td>2.313707</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>7.5040</td>\n",
+       "      <td>1.480633</td>\n",
+       "      <td>1.610574</td>\n",
+       "      <td>0.833552</td>\n",
+       "      <td>0.627163</td>\n",
+       "      <td>0.475540</td>\n",
+       "      <td>0.153527</td>\n",
+       "      <td>2.322715</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>7.4940</td>\n",
+       "      <td>1.564980</td>\n",
+       "      <td>1.516912</td>\n",
+       "      <td>0.858131</td>\n",
+       "      <td>0.620071</td>\n",
+       "      <td>0.290549</td>\n",
+       "      <td>0.367007</td>\n",
+       "      <td>2.276716</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Finland</td>\n",
+       "      <td>7.4690</td>\n",
+       "      <td>1.443572</td>\n",
+       "      <td>1.540247</td>\n",
+       "      <td>0.809158</td>\n",
+       "      <td>0.617951</td>\n",
+       "      <td>0.245483</td>\n",
+       "      <td>0.382612</td>\n",
+       "      <td>2.430182</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>Botswana</td>\n",
+       "      <td>3.4789</td>\n",
+       "      <td>0.997549</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.494102</td>\n",
+       "      <td>0.509089</td>\n",
+       "      <td>0.033407</td>\n",
+       "      <td>0.101786</td>\n",
+       "      <td>0.257241</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.085695</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>788</th>\n",
+       "      <td>Tanzania</td>\n",
+       "      <td>3.4762</td>\n",
+       "      <td>0.457163</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.442678</td>\n",
+       "      <td>0.509343</td>\n",
+       "      <td>0.271541</td>\n",
+       "      <td>0.203881</td>\n",
+       "      <td>0.718963</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.872675</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>789</th>\n",
+       "      <td>Rwanda</td>\n",
+       "      <td>3.3123</td>\n",
+       "      <td>0.343243</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.572383</td>\n",
+       "      <td>0.604088</td>\n",
+       "      <td>0.235705</td>\n",
+       "      <td>0.485542</td>\n",
+       "      <td>0.548445</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.522876</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>3.2992</td>\n",
+       "      <td>0.425564</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.375038</td>\n",
+       "      <td>0.377405</td>\n",
+       "      <td>0.151349</td>\n",
+       "      <td>0.080929</td>\n",
+       "      <td>0.841031</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.047835</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>791</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2.5669</td>\n",
+       "      <td>0.300706</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.266052</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.135235</td>\n",
+       "      <td>0.001226</td>\n",
+       "      <td>1.507236</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.356434</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>792 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Country  happiness_score  gdp_per_capita    family    health  \\\n",
+       "0         Norway           7.5370        1.616463  1.533524  0.796667   \n",
+       "1        Denmark           7.5220        1.482383  1.551122  0.792566   \n",
+       "2        Iceland           7.5040        1.480633  1.610574  0.833552   \n",
+       "3    Switzerland           7.4940        1.564980  1.516912  0.858131   \n",
+       "4        Finland           7.4690        1.443572  1.540247  0.809158   \n",
+       "..           ...              ...             ...       ...       ...   \n",
+       "787     Botswana           3.4789        0.997549  0.000000  0.494102   \n",
+       "788     Tanzania           3.4762        0.457163  0.000000  0.442678   \n",
+       "789       Rwanda           3.3123        0.343243  0.000000  0.572383   \n",
+       "790     Zimbabwe           3.2992        0.425564  0.000000  0.375038   \n",
+       "791  Afghanistan           2.5669        0.300706  0.000000  0.266052   \n",
+       "\n",
+       "      freedom  generosity  government_trust  dystopia_residual continent  \\\n",
+       "0    0.635423    0.362012          0.315964           2.277027    Europe   \n",
+       "1    0.626007    0.355280          0.400770           2.313707    Europe   \n",
+       "2    0.627163    0.475540          0.153527           2.322715    Europe   \n",
+       "3    0.620071    0.290549          0.367007           2.276716    Europe   \n",
+       "4    0.617951    0.245483          0.382612           2.430182    Europe   \n",
+       "..        ...         ...               ...                ...       ...   \n",
+       "787  0.509089    0.033407          0.101786           0.257241    Africa   \n",
+       "788  0.509343    0.271541          0.203881           0.718963    Africa   \n",
+       "789  0.604088    0.235705          0.485542           0.548445    Africa   \n",
+       "790  0.377405    0.151349          0.080929           0.841031    Africa   \n",
+       "791  0.000000    0.135235          0.001226           1.507236      Asia   \n",
+       "\n",
+       "     Year  social_support  cpi_score  \n",
+       "0    2015        0.000000         88  \n",
+       "1    2015        0.000000         91  \n",
+       "2    2015        0.000000         79  \n",
+       "3    2015        0.000000         86  \n",
+       "4    2015        0.000000         90  \n",
+       "..    ...             ...        ...  \n",
+       "787  2020        1.085695         60  \n",
+       "788  2020        0.872675         38  \n",
+       "789  2020        0.522876         54  \n",
+       "790  2020        1.047835         24  \n",
+       "791  2020        0.356434         19  \n",
+       "\n",
+       "[792 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#drop_duplicates()\n",
+    "df.drop_duplicates(inplace=True)\n",
+    "df\n",
+    "\n",
+    "#no duplicates in the dat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "c45e3877",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "continent\n",
+       "Africa           0.403690\n",
+       "Asia             0.471045\n",
+       "Australia        0.663723\n",
+       "Europe           0.590485\n",
+       "North America    0.580827\n",
+       "South America    0.540228\n",
+       "Name: family, dtype: float64"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#groupby()\n",
+    "df.groupby(by='continent').family.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "8620a24d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lkey</th>\n",
+       "      <th>value_x</th>\n",
+       "      <th>rkey</th>\n",
+       "      <th>value_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>1</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>5</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>5</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>baz</td>\n",
+       "      <td>3</td>\n",
+       "      <td>baz</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  lkey  value_x rkey  value_y\n",
+       "0  foo        1  foo        5\n",
+       "1  foo        1  foo        8\n",
+       "2  foo        5  foo        5\n",
+       "3  foo        5  foo        8\n",
+       "4  bar        2  bar        6\n",
+       "5  baz        3  baz        7"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#merge()\n",
+    "table1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'], 'value':[1,2,3,5]})\n",
+    "table2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'], 'value':[5,6,7,8]})\n",
+    "table1.merge(table2, left_on='lkey', right_on='rkey')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "bb212a67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                           395          657          519          259  \\\n",
+      "Country            Afghanistan  Afghanistan  Afghanistan  Afghanistan   \n",
+      "happiness_score          3.203         3.36        3.632        3.575   \n",
+      "gdp_per_capita            0.35      0.38227        0.332      0.31982   \n",
+      "family                     0.0      0.11037          0.0      0.30285   \n",
+      "health                   0.361      0.17344        0.255      0.30335   \n",
+      "freedom                    0.0       0.1643        0.085      0.23414   \n",
+      "generosity               0.158      0.31268        0.191       0.3651   \n",
+      "government_trust         0.025      0.07112        0.036      0.09719   \n",
+      "dystopia_residual          0.0      2.14558          0.0       1.9521   \n",
+      "continent                 Asia         Asia         Asia         Asia   \n",
+      "Year                      2017         2019         2018         2016   \n",
+      "social_support           0.517          0.0        0.537          0.0   \n",
+      "cpi_score                   15           16           16           15   \n",
+      "\n",
+      "                           791          120      493       753       96   \\\n",
+      "Country            Afghanistan  Afghanistan  Albania   Albania   Albania   \n",
+      "happiness_score         2.5669        3.794    4.586    4.8827     4.644   \n",
+      "gdp_per_capita        0.300706     0.401477    0.916  0.906653  0.996193   \n",
+      "family                     0.0     0.581543      0.0       0.0  0.803685   \n",
+      "health                0.266052     0.180747     0.79   0.84633   0.73116   \n",
+      "freedom                    0.0      0.10618    0.419  0.461946  0.381499   \n",
+      "generosity            0.135235     0.311871    0.149  0.171028  0.201313   \n",
+      "government_trust      0.001226     0.061158    0.032  0.025361  0.039864   \n",
+      "dystopia_residual     1.507236     2.150801      0.0  1.640897  1.490442   \n",
+      "continent                 Asia         Asia   Europe    Europe    Europe   \n",
+      "Year                      2020         2015     2018      2020      2015   \n",
+      "social_support        0.356434          0.0    0.817  0.830484       0.0   \n",
+      "cpi_score                   19           11       36        36        36   \n",
+      "\n",
+      "                       620  ...      206       101      618     382       518  \\\n",
+      "Country            Albania  ...   Zambia    Zambia   Zambia  Zambia  Zimbabwe   \n",
+      "happiness_score      4.655  ...    5.129     4.514    4.795   4.107     3.692   \n",
+      "gdp_per_capita      0.9553  ...  0.47038  0.636407  0.61202   0.578     0.357   \n",
+      "family             0.50163  ...  0.91612  1.003187   0.6376     0.0       0.0   \n",
+      "health             0.73007  ...  0.29924  0.257836  0.23573   0.426     0.248   \n",
+      "freedom            0.31866  ...  0.48827  0.461603  0.42662   0.431     0.406   \n",
+      "generosity          0.1684  ...  0.19591   0.24958  0.17866   0.247     0.132   \n",
+      "government_trust   0.05301  ...  0.12468  0.078214  0.11479   0.087     0.099   \n",
+      "dystopia_residual  1.92816  ...   2.6343  1.826705  2.58991     0.0       0.0   \n",
+      "continent           Europe  ...   Africa    Africa   Africa  Africa    Africa   \n",
+      "Year                  2019  ...     2016      2015     2019    2017      2018   \n",
+      "social_support         0.0  ...      0.0       0.0      0.0   1.058     1.094   \n",
+      "cpi_score               35  ...       38        38       34      37        22   \n",
+      "\n",
+      "                        229       119       388       790       639  \n",
+      "Country            Zimbabwe  Zimbabwe  Zimbabwe  Zimbabwe  Zimbabwe  \n",
+      "happiness_score        4.61     3.875     3.663    3.2992     4.193  \n",
+      "gdp_per_capita        0.271  0.375847     0.366  0.425564   0.35041  \n",
+      "family              1.03276  1.083096       0.0       0.0   0.71478  \n",
+      "health              0.33475  0.196764     0.433  0.375038    0.1595  \n",
+      "freedom             0.25861  0.336384     0.361  0.377405   0.25429  \n",
+      "generosity          0.18987  0.189143     0.151  0.151349   0.18503  \n",
+      "government_trust    0.08079  0.095375     0.089  0.080929   0.08582  \n",
+      "dystopia_residual   2.44191   1.59797       0.0  0.841031    2.4427  \n",
+      "continent            Africa    Africa    Africa    Africa    Africa  \n",
+      "Year                   2016      2015      2017      2020      2019  \n",
+      "social_support          0.0       0.0     1.114  1.047835       0.0  \n",
+      "cpi_score                22        21        22        24        24  \n",
+      "\n",
+      "[13 rows x 792 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#sort_values()\n",
+    "print(df.sort_values(by='Country', inplace=False).T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "fba8593f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>3.8750</td>\n",
+       "      <td>0.375847</td>\n",
+       "      <td>1.083096</td>\n",
+       "      <td>0.196764</td>\n",
+       "      <td>0.336384</td>\n",
+       "      <td>0.189143</td>\n",
+       "      <td>0.095375</td>\n",
+       "      <td>1.597970</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>3.6630</td>\n",
+       "      <td>0.366000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.433000</td>\n",
+       "      <td>0.361000</td>\n",
+       "      <td>0.151000</td>\n",
+       "      <td>0.089000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1.114000</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>3.2992</td>\n",
+       "      <td>0.425564</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.375038</td>\n",
+       "      <td>0.377405</td>\n",
+       "      <td>0.151349</td>\n",
+       "      <td>0.080929</td>\n",
+       "      <td>0.841031</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.047835</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>3.6920</td>\n",
+       "      <td>0.357000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.248000</td>\n",
+       "      <td>0.406000</td>\n",
+       "      <td>0.132000</td>\n",
+       "      <td>0.099000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1.094000</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>4.1930</td>\n",
+       "      <td>0.350410</td>\n",
+       "      <td>0.714780</td>\n",
+       "      <td>0.159500</td>\n",
+       "      <td>0.254290</td>\n",
+       "      <td>0.185030</td>\n",
+       "      <td>0.085820</td>\n",
+       "      <td>2.442700</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Country  happiness_score  gdp_per_capita    family    health   freedom  \\\n",
+       "119  Zimbabwe           3.8750        0.375847  1.083096  0.196764  0.336384   \n",
+       "388  Zimbabwe           3.6630        0.366000  0.000000  0.433000  0.361000   \n",
+       "790  Zimbabwe           3.2992        0.425564  0.000000  0.375038  0.377405   \n",
+       "518  Zimbabwe           3.6920        0.357000  0.000000  0.248000  0.406000   \n",
+       "639  Zimbabwe           4.1930        0.350410  0.714780  0.159500  0.254290   \n",
+       "\n",
+       "     generosity  government_trust  dystopia_residual continent  Year  \\\n",
+       "119    0.189143          0.095375           1.597970    Africa  2015   \n",
+       "388    0.151000          0.089000           0.000000    Africa  2017   \n",
+       "790    0.151349          0.080929           0.841031    Africa  2020   \n",
+       "518    0.132000          0.099000           0.000000    Africa  2018   \n",
+       "639    0.185030          0.085820           2.442700    Africa  2019   \n",
+       "\n",
+       "     social_support  cpi_score  \n",
+       "119        0.000000         21  \n",
+       "388        1.114000         22  \n",
+       "790        1.047835         24  \n",
+       "518        1.094000         22  \n",
+       "639        0.000000         24  "
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#tail\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "02ea1d43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(792, 13)"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#shape\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "c2d9125a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>5.2650</td>\n",
+       "      <td>0.696000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.245000</td>\n",
+       "      <td>0.426000</td>\n",
+       "      <td>0.215000</td>\n",
+       "      <td>0.041000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1.111000</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>6.4030</td>\n",
+       "      <td>1.384398</td>\n",
+       "      <td>1.532091</td>\n",
+       "      <td>0.888961</td>\n",
+       "      <td>0.408781</td>\n",
+       "      <td>0.190134</td>\n",
+       "      <td>0.070914</td>\n",
+       "      <td>1.927758</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>Moldova</td>\n",
+       "      <td>5.8380</td>\n",
+       "      <td>0.728871</td>\n",
+       "      <td>1.251826</td>\n",
+       "      <td>0.589465</td>\n",
+       "      <td>0.240729</td>\n",
+       "      <td>0.208779</td>\n",
+       "      <td>0.010091</td>\n",
+       "      <td>2.807808</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>Tunisia</td>\n",
+       "      <td>4.4610</td>\n",
+       "      <td>0.921000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.815000</td>\n",
+       "      <td>0.167000</td>\n",
+       "      <td>0.059000</td>\n",
+       "      <td>0.055000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>Bulgaria</td>\n",
+       "      <td>4.2180</td>\n",
+       "      <td>1.012160</td>\n",
+       "      <td>1.106140</td>\n",
+       "      <td>0.766490</td>\n",
+       "      <td>0.305870</td>\n",
+       "      <td>0.119210</td>\n",
+       "      <td>0.008720</td>\n",
+       "      <td>0.899910</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Italy</td>\n",
+       "      <td>5.9640</td>\n",
+       "      <td>1.395067</td>\n",
+       "      <td>1.444923</td>\n",
+       "      <td>0.853144</td>\n",
+       "      <td>0.256451</td>\n",
+       "      <td>0.172790</td>\n",
+       "      <td>0.028028</td>\n",
+       "      <td>1.813312</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>781</th>\n",
+       "      <td>Burundi</td>\n",
+       "      <td>3.7753</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.295213</td>\n",
+       "      <td>0.275399</td>\n",
+       "      <td>0.187402</td>\n",
+       "      <td>0.212187</td>\n",
+       "      <td>2.401507</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.403575</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>7.4270</td>\n",
+       "      <td>1.326290</td>\n",
+       "      <td>1.322610</td>\n",
+       "      <td>0.905630</td>\n",
+       "      <td>0.632970</td>\n",
+       "      <td>0.458110</td>\n",
+       "      <td>0.329570</td>\n",
+       "      <td>2.451760</td>\n",
+       "      <td>North America</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Country  happiness_score  gdp_per_capita    family    health   freedom  \\\n",
+       "340   Nigeria           5.2650        0.696000  0.000000  0.245000  0.426000   \n",
+       "31      Spain           6.4030        1.384398  1.532091  0.888961  0.408781   \n",
+       "49    Moldova           5.8380        0.728871  1.251826  0.589465  0.240729   \n",
+       "370   Tunisia           4.4610        0.921000  0.000000  0.815000  0.167000   \n",
+       "245  Bulgaria           4.2180        1.012160  1.106140  0.766490  0.305870   \n",
+       "43      Italy           5.9640        1.395067  1.444923  0.853144  0.256451   \n",
+       "781   Burundi           3.7753        0.000000  0.000000  0.295213  0.275399   \n",
+       "136    Canada           7.4270        1.326290  1.322610  0.905630  0.632970   \n",
+       "\n",
+       "     generosity  government_trust  dystopia_residual      continent  Year  \\\n",
+       "340    0.215000          0.041000           0.000000         Africa  2017   \n",
+       "31     0.190134          0.070914           1.927758         Europe  2015   \n",
+       "49     0.208779          0.010091           2.807808         Europe  2015   \n",
+       "370    0.059000          0.055000           0.000000         Africa  2017   \n",
+       "245    0.119210          0.008720           0.899910         Europe  2016   \n",
+       "43     0.172790          0.028028           1.813312         Europe  2015   \n",
+       "781    0.187402          0.212187           2.401507         Africa  2020   \n",
+       "136    0.458110          0.329570           2.451760  North America  2016   \n",
+       "\n",
+       "     social_support  cpi_score  \n",
+       "340        1.111000         27  \n",
+       "31         0.000000         58  \n",
+       "49         0.000000         33  \n",
+       "370        1.000000         42  \n",
+       "245        0.000000         41  \n",
+       "43         0.000000         44  \n",
+       "781        0.403575         19  \n",
+       "136        0.000000         82  "
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sample\n",
+    "df.sample(n=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "cac29f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country              0\n",
+       "happiness_score      0\n",
+       "gdp_per_capita       0\n",
+       "family               0\n",
+       "health               0\n",
+       "freedom              0\n",
+       "generosity           0\n",
+       "government_trust     0\n",
+       "dystopia_residual    0\n",
+       "continent            0\n",
+       "Year                 0\n",
+       "social_support       0\n",
+       "cpi_score            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#df.isnull().sum()\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "7720b777",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country              132\n",
+       "happiness_score      747\n",
+       "gdp_per_capita       767\n",
+       "family               396\n",
+       "health               733\n",
+       "freedom              734\n",
+       "generosity           707\n",
+       "government_trust     682\n",
+       "dystopia_residual    529\n",
+       "continent              6\n",
+       "Year                   6\n",
+       "social_support       360\n",
+       "cpi_score             77\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#nunique()\n",
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14bf43dd",
+   "metadata": {},
+   "source": [
+    "METHOD 20 #Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "28ff7ee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.2030</td>\n",
+       "      <td>0.350000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.361000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.158000</td>\n",
+       "      <td>0.025000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>0.517000</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.3600</td>\n",
+       "      <td>0.382270</td>\n",
+       "      <td>0.110370</td>\n",
+       "      <td>0.173440</td>\n",
+       "      <td>0.164300</td>\n",
+       "      <td>0.312680</td>\n",
+       "      <td>0.071120</td>\n",
+       "      <td>2.145580</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.6320</td>\n",
+       "      <td>0.332000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.085000</td>\n",
+       "      <td>0.191000</td>\n",
+       "      <td>0.036000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>0.537000</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.5750</td>\n",
+       "      <td>0.319820</td>\n",
+       "      <td>0.302850</td>\n",
+       "      <td>0.303350</td>\n",
+       "      <td>0.234140</td>\n",
+       "      <td>0.365100</td>\n",
+       "      <td>0.097190</td>\n",
+       "      <td>1.952100</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.7940</td>\n",
+       "      <td>0.401477</td>\n",
+       "      <td>0.581543</td>\n",
+       "      <td>0.180747</td>\n",
+       "      <td>0.106180</td>\n",
+       "      <td>0.311871</td>\n",
+       "      <td>0.061158</td>\n",
+       "      <td>2.150801</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>4.5860</td>\n",
+       "      <td>0.916000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.790000</td>\n",
+       "      <td>0.419000</td>\n",
+       "      <td>0.149000</td>\n",
+       "      <td>0.032000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>0.817000</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>4.8827</td>\n",
+       "      <td>0.906653</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.846330</td>\n",
+       "      <td>0.461946</td>\n",
+       "      <td>0.171028</td>\n",
+       "      <td>0.025361</td>\n",
+       "      <td>1.640897</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.830484</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>4.6440</td>\n",
+       "      <td>0.996193</td>\n",
+       "      <td>0.803685</td>\n",
+       "      <td>0.731160</td>\n",
+       "      <td>0.381499</td>\n",
+       "      <td>0.201313</td>\n",
+       "      <td>0.039864</td>\n",
+       "      <td>1.490442</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>4.6550</td>\n",
+       "      <td>0.955300</td>\n",
+       "      <td>0.501630</td>\n",
+       "      <td>0.730070</td>\n",
+       "      <td>0.318660</td>\n",
+       "      <td>0.168400</td>\n",
+       "      <td>0.053010</td>\n",
+       "      <td>1.928160</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>4.9590</td>\n",
+       "      <td>0.878670</td>\n",
+       "      <td>0.804340</td>\n",
+       "      <td>0.813250</td>\n",
+       "      <td>0.357330</td>\n",
+       "      <td>0.142720</td>\n",
+       "      <td>0.064130</td>\n",
+       "      <td>1.898940</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Country  happiness_score  gdp_per_capita    family    health  \\\n",
+       "395  Afghanistan           3.2030        0.350000  0.000000  0.361000   \n",
+       "657  Afghanistan           3.3600        0.382270  0.110370  0.173440   \n",
+       "519  Afghanistan           3.6320        0.332000  0.000000  0.255000   \n",
+       "259  Afghanistan           3.5750        0.319820  0.302850  0.303350   \n",
+       "120  Afghanistan           3.7940        0.401477  0.581543  0.180747   \n",
+       "493      Albania           4.5860        0.916000  0.000000  0.790000   \n",
+       "753      Albania           4.8827        0.906653  0.000000  0.846330   \n",
+       "96       Albania           4.6440        0.996193  0.803685  0.731160   \n",
+       "620      Albania           4.6550        0.955300  0.501630  0.730070   \n",
+       "213      Albania           4.9590        0.878670  0.804340  0.813250   \n",
+       "\n",
+       "      freedom  generosity  government_trust  dystopia_residual continent  \\\n",
+       "395  0.000000    0.158000          0.025000           0.000000      Asia   \n",
+       "657  0.164300    0.312680          0.071120           2.145580      Asia   \n",
+       "519  0.085000    0.191000          0.036000           0.000000      Asia   \n",
+       "259  0.234140    0.365100          0.097190           1.952100      Asia   \n",
+       "120  0.106180    0.311871          0.061158           2.150801      Asia   \n",
+       "493  0.419000    0.149000          0.032000           0.000000    Europe   \n",
+       "753  0.461946    0.171028          0.025361           1.640897    Europe   \n",
+       "96   0.381499    0.201313          0.039864           1.490442    Europe   \n",
+       "620  0.318660    0.168400          0.053010           1.928160    Europe   \n",
+       "213  0.357330    0.142720          0.064130           1.898940    Europe   \n",
+       "\n",
+       "     Year  social_support  cpi_score  \n",
+       "395  2017        0.517000         15  \n",
+       "657  2019        0.000000         16  \n",
+       "519  2018        0.537000         16  \n",
+       "259  2016        0.000000         15  \n",
+       "120  2015        0.000000         11  \n",
+       "493  2018        0.817000         36  \n",
+       "753  2020        0.830484         36  \n",
+       "96   2015        0.000000         36  \n",
+       "620  2019        0.000000         35  \n",
+       "213  2016        0.000000         39  "
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.query('3 < happiness_score < 8')[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd2768ca",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/DEBORAH.O (1).py b/DEBORAH.O (1).py
new file mode 100644
index 0000000..dfd3662
--- /dev/null
+++ b/DEBORAH.O (1).py	
@@ -0,0 +1,192 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # print all elements of a list using for loop
+
+# In[1]:
+
+
+myList = ['James', 'Otobong', 'Temitayo', 'Blessing', 'Benard', 'Anthony', 'Isangha', 'Hilary', 'Opeoluwa', 'Victory']
+
+for student in myList:
+    print(student)
+
+
+# # using range(1,101), make two list, one containing all even numbers and other containing all odd numbers
+# 
+# 
+
+# In[21]:
+
+
+lowerRange = 1
+upperRange = 101
+i = 2
+
+print("Even numbers between", lowerRange, "and", upperRange, "are:")
+
+for Z in range(lowerRange, upperRange + 1):
+      if Z > 1:
+            if (Z % i) == 0:
+                print(Z)
+
+
+# In[20]:
+
+
+lowerRange = 1
+upperRange = 101
+i = 2
+
+print("Odd numbers between", lowerRange, "and", upperRange, "are:")
+
+for Z in range(lowerRange, upperRange + 1):
+      if Z >= 1:
+            if (Z % i) != 0:
+                print(Z)
+
+
+# # A company decided to give bonus of 5% to employee if his/her year of service is more than 5 years. Ask user for their salary and year of service and print the net bonus amount
+
+# In[41]:
+
+
+target = 5
+def bonusCalc(sal,year):
+    if year > target:
+        netBonus = sal*0.05
+        print('Your net bonus is: ', netBonus)
+        return netBonus
+    else:
+        print('Sorry, but at this time, you are not qualified for bonus')
+    
+
+
+# In[42]:
+
+
+salary = input('How much is your salary')
+Year = input('how long is your year of service in this organization')
+
+bonusCalc(int(salary),int(Year))
+
+
+# # Take Input of age 3 people by user and determine oldest and youngest among them
+
+# In[52]:
+
+
+def ageRange(age1,age2,age3):
+    if (age1 > age2) & (age1 > age3):
+        print('the oldest is the first person')
+        if age2 < age3:
+                print('the youngest is the second person')
+        else:
+                print('the youngest is the third person')
+                
+    elif (age2 > age1) & (age2 > age3):
+        print('the oldest pesron is the second person')
+        if age1 < age3:
+                print('the youngest is the first person')
+        else:
+                print('the youngest is the third person')
+    else:
+        print('the oldest is the third person')
+        if age1 < age2:
+                print('the youngest is the first person')
+        else:
+                print('the youngest is the second person')
+        
+    
+
+
+# In[72]:
+
+
+firstPerson = input('how old are you? ')
+secondPerson = input('how old are you? ')
+thirdPerson = input('how old are you? ')
+
+ageRange(int(firstPerson), int(secondPerson), int(thirdPerson))
+
+
+# # A school has following rules for grading system:
+#     Below 25 -F
+#     25 to 45 -E
+#     45 to 50 -D
+#     50 to 60 -C
+#     60 to 80 -B
+#     Above 80 -A
+
+# In[33]:
+
+
+def scoreGrade(score):
+    if score > 80:
+        print("your grade is 'A'")
+    elif score >60 and score <=80:
+        print("your grade is 'B'")
+    elif score >50 and score <=60:
+        print("your grade is 'C'")
+    elif score >45 and score <=50:
+        print("your grade is 'D'")
+    elif score >25 and score <=45:
+            print("your grade is 'E'")
+
+    else: 
+        print("your grade is 'F'")
+#return grd     
+
+
+# In[34]:
+
+
+score = input('what was your score')  
+scoreGrade(int(score))
+
+
+# # Write a Python script to merge two python dictionaries
+
+# In[57]:
+
+
+
+dict2 = {'CAD':1.52, 'EUR':1.14, 'Tola':4, 'Temmy':40}
+
+dict1.update(dict2)
+print(dict1)dict1 = {'NGN':470, 'GBP':0.99, 'MXN':20.1, 'ZAR':17.99, 'ZMK':15830, 'GHS':10.5, 'DEM':1.99, 'USD':1}
+
+
+# # Write a Python program to remove a key from a dictionary
+
+# In[64]:
+
+
+mergedDict = {'NGN': 470, 'GBP': 0.99, 'MXN': 20.1, 'ZAR': 17.99, 'ZMK': 15830, 'GHS': 10.5, 'DEM': 1.99, 
+              'USD': 1, 'CAD': 1.52, 'EUR': 1.14, 'Tola': 4, 'Temmy': 40}
+
+mergedDict.pop("Temmy")
+print(mergedDict)
+
+
+# # Write a Python program to get the largest number from a list
+
+# In[71]:
+
+
+listEsden = [90, 28, 31, 23, 25, 59, 65]
+
+def largestNum(listEsden):
+    max = listEsden[0]
+    for a in listEsden:
+        if a > max:
+            max = a 
+    return max
+print(largestNum(listEsden))
+
+
+# In[ ]:
+
+
+
+
diff --git a/DEBORAH.O.ipynb b/DEBORAH.O.ipynb
new file mode 100644
index 0000000..7efb04d
--- /dev/null
+++ b/DEBORAH.O.ipynb
@@ -0,0 +1,511 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d54d45ce",
+   "metadata": {},
+   "source": [
+    "# print all elements of a list using for loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6f35a4b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "James\n",
+      "Otobong\n",
+      "Temitayo\n",
+      "Blessing\n",
+      "Benard\n",
+      "Anthony\n",
+      "Isangha\n",
+      "Hilary\n",
+      "Opeoluwa\n",
+      "Victory\n"
+     ]
+    }
+   ],
+   "source": [
+    "myList = ['James', 'Otobong', 'Temitayo', 'Blessing', 'Benard', 'Anthony', 'Isangha', 'Hilary', 'Opeoluwa', 'Victory']\n",
+    "\n",
+    "for student in myList:\n",
+    "    print(student)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a2d5b07",
+   "metadata": {},
+   "source": [
+    "# using range(1,101), make two list, one containing all even numbers and other containing all odd numbers\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5cd2c9af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Even numbers between 1 and 101 are:\n",
+      "2\n",
+      "4\n",
+      "6\n",
+      "8\n",
+      "10\n",
+      "12\n",
+      "14\n",
+      "16\n",
+      "18\n",
+      "20\n",
+      "22\n",
+      "24\n",
+      "26\n",
+      "28\n",
+      "30\n",
+      "32\n",
+      "34\n",
+      "36\n",
+      "38\n",
+      "40\n",
+      "42\n",
+      "44\n",
+      "46\n",
+      "48\n",
+      "50\n",
+      "52\n",
+      "54\n",
+      "56\n",
+      "58\n",
+      "60\n",
+      "62\n",
+      "64\n",
+      "66\n",
+      "68\n",
+      "70\n",
+      "72\n",
+      "74\n",
+      "76\n",
+      "78\n",
+      "80\n",
+      "82\n",
+      "84\n",
+      "86\n",
+      "88\n",
+      "90\n",
+      "92\n",
+      "94\n",
+      "96\n",
+      "98\n",
+      "100\n"
+     ]
+    }
+   ],
+   "source": [
+    "lowerRange = 1\n",
+    "upperRange = 101\n",
+    "i = 2\n",
+    "\n",
+    "print(\"Even numbers between\", lowerRange, \"and\", upperRange, \"are:\")\n",
+    "\n",
+    "for Z in range(lowerRange, upperRange + 1):\n",
+    "      if Z > 1:\n",
+    "            if (Z % i) == 0:\n",
+    "                print(Z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7a4e6e34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Odd numbers between 1 and 101 are:\n",
+      "1\n",
+      "3\n",
+      "5\n",
+      "7\n",
+      "9\n",
+      "11\n",
+      "13\n",
+      "15\n",
+      "17\n",
+      "19\n",
+      "21\n",
+      "23\n",
+      "25\n",
+      "27\n",
+      "29\n",
+      "31\n",
+      "33\n",
+      "35\n",
+      "37\n",
+      "39\n",
+      "41\n",
+      "43\n",
+      "45\n",
+      "47\n",
+      "49\n",
+      "51\n",
+      "53\n",
+      "55\n",
+      "57\n",
+      "59\n",
+      "61\n",
+      "63\n",
+      "65\n",
+      "67\n",
+      "69\n",
+      "71\n",
+      "73\n",
+      "75\n",
+      "77\n",
+      "79\n",
+      "81\n",
+      "83\n",
+      "85\n",
+      "87\n",
+      "89\n",
+      "91\n",
+      "93\n",
+      "95\n",
+      "97\n",
+      "99\n",
+      "101\n"
+     ]
+    }
+   ],
+   "source": [
+    "lowerRange = 1\n",
+    "upperRange = 101\n",
+    "i = 2\n",
+    "\n",
+    "print(\"Odd numbers between\", lowerRange, \"and\", upperRange, \"are:\")\n",
+    "\n",
+    "for Z in range(lowerRange, upperRange + 1):\n",
+    "      if Z >= 1:\n",
+    "            if (Z % i) != 0:\n",
+    "                print(Z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1ea7eda",
+   "metadata": {},
+   "source": [
+    "# A company decided to give bonus of 5% to employee if his/her year of service is more than 5 years. Ask user for their salary and year of service and print the net bonus amount"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "48ad05e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = 5\n",
+    "def bonusCalc(sal,year):\n",
+    "    if year > target:\n",
+    "        netBonus = sal*0.05\n",
+    "        print('Your net bonus is: ', netBonus)\n",
+    "        return netBonus\n",
+    "    else:\n",
+    "        print('Sorry, but at this time, you are not qualified for bonus')\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "9d86d7cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "How much is your salary40000\n",
+      "how long is your year of service in this organization10\n",
+      "Your net bonus is:  2000.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2000.0"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "salary = input('How much is your salary')\n",
+    "Year = input('how long is your year of service in this organization')\n",
+    "\n",
+    "bonusCalc(int(salary),int(Year))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15173add",
+   "metadata": {},
+   "source": [
+    "# Take Input of age 3 people by user and determine oldest and youngest among them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "690d4cc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ageRange(age1,age2,age3):\n",
+    "    if (age1 > age2) & (age1 > age3):\n",
+    "        print('the oldest is the first person')\n",
+    "        if age2 < age3:\n",
+    "                print('the youngest is the second person')\n",
+    "        else:\n",
+    "                print('the youngest is the third person')\n",
+    "                \n",
+    "    elif (age2 > age1) & (age2 > age3):\n",
+    "        print('the oldest pesron is the second person')\n",
+    "        if age1 < age3:\n",
+    "                print('the youngest is the first person')\n",
+    "        else:\n",
+    "                print('the youngest is the third person')\n",
+    "    else:\n",
+    "        print('the oldest is the third person')\n",
+    "        if age1 < age2:\n",
+    "                print('the youngest is the first person')\n",
+    "        else:\n",
+    "                print('the youngest is the second person')\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "bd0d45ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "how old are you? 56\n",
+      "how old are you? 39\n",
+      "how old are you? 10\n",
+      "the oldest is the first person\n",
+      "the youngest is the third person\n"
+     ]
+    }
+   ],
+   "source": [
+    "firstPerson = input('how old are you? ')\n",
+    "secondPerson = input('how old are you? ')\n",
+    "thirdPerson = input('how old are you? ')\n",
+    "\n",
+    "ageRange(int(firstPerson), int(secondPerson), int(thirdPerson))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e959fee6",
+   "metadata": {},
+   "source": [
+    "# A school has following rules for grading system:\n",
+    "    Below 25 -F\n",
+    "    25 to 45 -E\n",
+    "    45 to 50 -D\n",
+    "    50 to 60 -C\n",
+    "    60 to 80 -B\n",
+    "    Above 80 -A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "3af90ef8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def scoreGrade(score):\n",
+    "    if score > 80:\n",
+    "        print(\"your grade is 'A'\")\n",
+    "    elif score >60 and score <=80:\n",
+    "        print(\"your grade is 'B'\")\n",
+    "    elif score >50 and score <=60:\n",
+    "        print(\"your grade is 'C'\")\n",
+    "    elif score >45 and score <=50:\n",
+    "        print(\"your grade is 'D'\")\n",
+    "    elif score >25 and score <=45:\n",
+    "            print(\"your grade is 'E'\")\n",
+    "\n",
+    "    else: \n",
+    "        print(\"your grade is 'F'\")\n",
+    "#return grd     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "1f9d1330",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "what was your score25\n",
+      "your grade is 'F'\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = input('what was your score')  \n",
+    "scoreGrade(int(score))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b793a6cb",
+   "metadata": {},
+   "source": [
+    "# Write a Python script to merge two python dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "f84cdeb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'NGN': 470, 'GBP': 0.99, 'MXN': 20.1, 'ZAR': 17.99, 'ZMK': 15830, 'GHS': 10.5, 'DEM': 1.99, 'USD': 1, 'CAD': 1.52, 'EUR': 1.14, 'Tola': 4, 'Temmy': 40}\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "dict2 = {'CAD':1.52, 'EUR':1.14, 'Tola':4, 'Temmy':40}\n",
+    "\n",
+    "dict1.update(dict2)\n",
+    "print(dict1)dict1 = {'NGN':470, 'GBP':0.99, 'MXN':20.1, 'ZAR':17.99, 'ZMK':15830, 'GHS':10.5, 'DEM':1.99, 'USD':1}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1417b3e7",
+   "metadata": {},
+   "source": [
+    "# Write a Python program to remove a key from a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "64b1b50d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'NGN': 470, 'GBP': 0.99, 'MXN': 20.1, 'ZAR': 17.99, 'ZMK': 15830, 'GHS': 10.5, 'DEM': 1.99, 'USD': 1, 'CAD': 1.52, 'EUR': 1.14, 'Tola': 4}\n"
+     ]
+    }
+   ],
+   "source": [
+    "mergedDict = {'NGN': 470, 'GBP': 0.99, 'MXN': 20.1, 'ZAR': 17.99, 'ZMK': 15830, 'GHS': 10.5, 'DEM': 1.99, \n",
+    "              'USD': 1, 'CAD': 1.52, 'EUR': 1.14, 'Tola': 4, 'Temmy': 40}\n",
+    "\n",
+    "mergedDict.pop(\"Temmy\")\n",
+    "print(mergedDict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67f563b9",
+   "metadata": {},
+   "source": [
+    "# Write a Python program to get the largest number from a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "d9f665f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "90\n"
+     ]
+    }
+   ],
+   "source": [
+    "listEsden = [90, 28, 31, 23, 25, 59, 65]\n",
+    "\n",
+    "def largestNum(listEsden):\n",
+    "    max = listEsden[0]\n",
+    "    for a in listEsden:\n",
+    "        if a > max:\n",
+    "            max = a \n",
+    "    return max\n",
+    "print(largestNum(listEsden))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03d0c431",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/RandomForest Classifier- Deborah O..ipynb b/RandomForest Classifier- Deborah O..ipynb
new file mode 100644
index 0000000..2b2f772
--- /dev/null
+++ b/RandomForest Classifier- Deborah O..ipynb	
@@ -0,0 +1,778 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2a423298",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23c36ef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris = datasets.load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "target_names = iris.target_names\n",
+    "target_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "0628e5d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1    2    3\n",
+       "0  5.1  3.5  1.4  0.2\n",
+       "1  4.9  3.0  1.4  0.2\n",
+       "2  4.7  3.2  1.3  0.2\n",
+       "3  4.6  3.1  1.5  0.2\n",
+       "4  5.0  3.6  1.4  0.2"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(iris.data)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cfc2217",
+   "metadata": {},
+   "source": [
+    "# splitting the dataset to training and testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b31cc670",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32dd69c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f9db476",
+   "metadata": {},
+   "source": [
+    "# Building the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2dc44869",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=50)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(n_estimators=50)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "model = RandomForestClassifier(n_estimators= 50)\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4f6486a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 2, 0, 0, 2, 0, 2, 2, 1, 1,\n",
+       "       1, 2, 2, 2, 1, 0, 1, 0, 2, 1, 1, 1, 2, 0, 1, 2, 2, 2, 2, 0, 2, 0,\n",
+       "       1])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0fa0752b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6.4, 2.9, 4.3, 1.3],\n",
+       "       [5.5, 2.6, 4.4, 1.2],\n",
+       "       [5.2, 4.1, 1.5, 0.1],\n",
+       "       [5.9, 3. , 4.2, 1.5],\n",
+       "       [4.4, 3.2, 1.3, 0.2],\n",
+       "       [5.6, 2.9, 3.6, 1.3],\n",
+       "       [6. , 2.9, 4.5, 1.5],\n",
+       "       [5.2, 3.4, 1.4, 0.2],\n",
+       "       [5.8, 4. , 1.2, 0.2],\n",
+       "       [5.5, 2.3, 4. , 1.3],\n",
+       "       [5.1, 3.8, 1.5, 0.3],\n",
+       "       [6. , 2.7, 5.1, 1.6],\n",
+       "       [4.3, 3. , 1.1, 0.1],\n",
+       "       [5.7, 2.5, 5. , 2. ],\n",
+       "       [4.6, 3.2, 1.4, 0.2],\n",
+       "       [5.4, 3.9, 1.7, 0.4],\n",
+       "       [6.8, 3.2, 5.9, 2.3],\n",
+       "       [4.6, 3.6, 1. , 0.2],\n",
+       "       [5.9, 3. , 5.1, 1.8],\n",
+       "       [5.8, 2.8, 5.1, 2.4],\n",
+       "       [5.5, 2.4, 3.7, 1. ],\n",
+       "       [6.7, 3. , 5. , 1.7],\n",
+       "       [6. , 3.4, 4.5, 1.6],\n",
+       "       [6.5, 3. , 5.5, 1.8],\n",
+       "       [6.9, 3.2, 5.7, 2.3],\n",
+       "       [7.7, 2.6, 6.9, 2.3],\n",
+       "       [6. , 2.2, 4. , 1. ],\n",
+       "       [4.4, 2.9, 1.4, 0.2],\n",
+       "       [5.8, 2.7, 3.9, 1.2],\n",
+       "       [4.8, 3.4, 1.9, 0.2],\n",
+       "       [6.1, 2.6, 5.6, 1.4],\n",
+       "       [6.1, 2.9, 4.7, 1.4],\n",
+       "       [6.2, 2.2, 4.5, 1.5],\n",
+       "       [5.8, 2.6, 4. , 1.2],\n",
+       "       [6.3, 2.8, 5.1, 1.5],\n",
+       "       [5.5, 4.2, 1.4, 0.2],\n",
+       "       [6.1, 2.8, 4.7, 1.2],\n",
+       "       [7.1, 3. , 5.9, 2.1],\n",
+       "       [6.7, 3.3, 5.7, 2.5],\n",
+       "       [7.6, 3. , 6.6, 2.1],\n",
+       "       [6.5, 3.2, 5.1, 2. ],\n",
+       "       [5.4, 3.4, 1.5, 0.4],\n",
+       "       [7.7, 2.8, 6.7, 2. ],\n",
+       "       [5.1, 3.8, 1.9, 0.4],\n",
+       "       [6.4, 3.2, 4.5, 1.5]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13adb2c4",
+   "metadata": {},
+   "source": [
+    "# Checking the model accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "01c195bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9555555555555556"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86db2eb0",
+   "metadata": {},
+   "source": [
+    "# predicting using the model built"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1c83e4f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 2, 0, 2, 0, 0, 2, 0, 2, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 1, 0, 1, 0, 2, 1, 1, 1, 2, 0, 1, 2, 2, 2, 2, 0, 2, 0,\n",
+       "       1])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predicted_value = model.predict(X_test)\n",
+    "predicted_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de9dafda",
+   "metadata": {},
+   "source": [
+    "# Validating the model using confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "97c3542b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[14,  0,  0],\n",
+       "       [ 0, 15,  2],\n",
+       "       [ 0,  0, 14]], dtype=int64)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "clfx = confusion_matrix(y_test, predicted_value)\n",
+    "clfx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "21759dca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(45, 4)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3510f4b0",
+   "metadata": {},
+   "source": [
+    "# Visualising the confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "af71d1a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xgpqbNr94/5vz+anfy9u1tv/tk3m71voygwAAAGS5xQgAANKKdCflfJEgAAAAWRIEAABIK9KnGOWLBAEAAMiSIAAAQEpiBgEAAKCKBAEAANLMIAAAAFTRIAAAAFluMQIAgBRDygAAANUkCAAAkGZIGQAAoIoEAQAAUhIJAgAAQBUJAgAApEkQAAAAqkgQAAAgxQwCAABANQkCAACkSRAAAACqSBAAACDFDAIAAEA1CQIAAKRIEAAAAKpJEAAAIEWCAAAAUE2CAAAAaUmm0BUUlAQBAADI0iAAAABZbjECAIAUQ8oAAADVJAgAAJCSVBpSBgAAiAgJAgAA5DCDAAAAUE2CAAAAKYmN0gAAAKpIEAAAIMUMAgAAQDUJAgAApNgHAQAAoJoEAQAAUpKk0BUUlgQBAADIkiAAAECKGQQAAIBqEgQAAEiRIAAAAFTboAZh1apV8cQTT8S4cePiX//6V0REfPTRR7F06dJaLQ4AAMivGt9i9N5778X3v//9mDdvXlRUVMThhx8eTZo0iZ/97GexfPnyuO222zZFnQAAkBcec1pDgwcPjs6dO8dnn30WjRo1yq4fd9xx8Ze//KVWiwMAAPKrxgnC1KlT47nnnouGDRvmrO+8887x4Ycf1lphAABQCIaUa6iysjJWr169xvoHH3wQTZo0qZWiAACAwqhxg3D44YfHqFGjsq8zmUwsXbo0RowYEUceeWRt1gYAAHmXJJm8HcWoxrcY3XzzzdGjR4/Yc889Y/ny5XHKKafEW2+9Fc2bN4977rlnU9QIAADkSY0bhLKyspg5c2bcc8898fLLL0dlZWUMHDgwTj311JyhZQAAqIuSykJXUFgbtJNyo0aNYsCAATFgwIDargcAACigGjcId91111e+f/rpp29wMQAAUGiVRTobkC81bhAGDx6c83rlypXxxRdfRMOGDWObbbbRIAAAQB1W4wbhs88+W2PtrbfeinPPPTcuvfTSWikKAAAKpVifLpQvNX7M6drsuuuuccMNN6yRLgAAALVj1apVceWVV0b79u2jUaNG8c1vfjOuueaaqKys3anqDRpSXpt69erFRx99VFunAwCAgijWnZRvvPHGuO222+LOO++M73znOzFt2rQ488wzo7S0tFb/or7GDcIjjzyS8zpJkpg/f36MGTMmunbtWmuFAQAA/+evf/1rHHvssXHUUUdFRES7du3innvuiWnTptXqdWrcIPTt2zfndSaTiRYtWsT3vve9+MUvflFbdQEAQEEkSf6uVVFRERUVFTlrJSUlUVJSssZnDz744Ljtttti9uzZ8e1vfzteeeWVmDp1aowaNapWa6pxg1Db9zgBAMCWqry8PK6++uqctREjRsTIkSPX+Ozll18eixcvjt133z3q1asXq1evjuuuuy5OPvnkWq2p1mYQAABgc5DPGYRhw4bF0KFDc9bWlh5ERNx7773xm9/8JiZNmhTf+c53YubMmTFkyJAoKyuL/v3711pN69Ug/GfRX+Wmm27a4GIAAGBLsq7bidbm0ksvjSuuuCJOOumkiIjYe++947333ovy8vL8NwgzZsxYr5NlMsU58Q0AAOurWHdS/uKLL2KrrXJ3KahXr15hHnP61FNP1epFAQCAmunTp09cd9110bZt2/jOd74TM2bMiJtuuikGDBhQq9cxgwAAAHXA6NGj46qrrorzzjsvFi5cGGVlZXH22WfHT37yk1q9zgY1CC+99FLcd999MW/evFixYkXOew888ECtFAYAAIWQFOktRk2aNIlRo0bV+mNN/9NWX/+RXL/73e+ia9euMWvWrHjwwQdj5cqVMWvWrHjyySejtLR0U9QIAADkSY0bhOuvvz5uvvnmePTRR6Nhw4Zxyy23xOuvvx4nnHBCtG3bdlPUCAAAeZMk+TuKUY0bhDlz5mS3dy4pKYlly5ZFJpOJiy66KG6//fZaLxAAAMifGjcITZs2jX/9618REbHjjjvGa6+9FhERn3/+eXzxxRe1Wx0AAORZZZLJ21GMajykfMghh8TkyZNj7733jhNOOCEGDx4cTz75ZEyePDkOO+ywTVEjAACQJ+vdIMycOTP222+/GDNmTCxfvjwiqraGbtCgQUydOjV+8IMfxFVXXbXJCgUAgHwo1qcY5UsmSdZvPGKrrbaKDh06xFlnnRWnnHJKUT+xqH7DHQtdAmtxztn94+Kh50SbNi3jH7Nmx8UXj4ipz71Y6LJI+fKjZwtdwhZt2sy/x4RJ/xOz3ng7Pvnnp3FL+VVxWLeDsu8Pv/YX8fBjT+T8zD577haT7hiV50r5T1327l/oEkg544LToseR3aLdLjtHxfKKeHXaazH62rHx3pz3C10aKdPmF+9/c2a0PTZv1+ow7+G8XWt9rfcMwnPPPRcdO3aMK664Itq0aROnnXaaHZZZb8cff0zc9IuRUX7Df0fnA46IqVNfjEf/8JvYaaeyQpcGRePLL5fHbrt8M/5r6Hnr/MzB3+0cTz/y2+wx9hc/zWOFUDd07LJf3DfhwTjzqLNj0IkXRb169WLM726KrRttXejSqCM8xWg9denSJe64445YsGBBjB07Nj744IPo2bNnfOtb34rrrrsuPvjgg01ZJ3XcRYN/FOMn/C7GT7gn3njj7bj4khHx/gcfxTlnn17o0qBoHNJl/7jwx/3j8O5d1/mZhg0aRPNmTbNH6XZN8lgh1A0XnnJJPPr7x+Kd2e/GW7PmxNUXlUebb7SOPfbdrdClQZ1Q46cYNWrUKPr37x9PP/10zJ49O04++eQYN25ctG/fPo488shNUSN1XIMGDaJjx31i8hNTctYnT54SXb7buUBVQd300oxXo9tRJ8VRJ50VI264Jf752eeFLgmK3rZNGkdExJLPlhS4EuqKLf0pRjVuENK+9a1vxRVXXBHDhw+P7bbbLh5//PHaqisiIt5///0YMGDAV36moqIilixZknOs51gFedK8edOoX79+LPx4Uc76woWLolXrlgWqCuqeg7/bOW4YcVn8evQNcen5Z8Vrr8+OgRdcEStWrCh0aVDUho48P2b87ZWY8+bcQpcCdcIGNwhTpkyJ/v37R+vWreOyyy6LH/zgB/Hcc8/VZm3x6aefxp133vmVnykvL4/S0tKcI6n8V63WQe34z8Ytk8lo5qAGevc8NA496IDY9ZvtovvB343bfvHTePf9D2PK8y8VujQoWpddf1Hssue3Yvi5Vxe6FOqQJMnk7ShGNdoH4f3334+JEyfGxIkTY+7cuXHQQQfF6NGj44QTTojGjRvX+OKPPPLIV77/zjvvfO05hg0bFkOHDs1Z26HZ7jWuhU1n0aJPY9WqVdGqdYuc9RYtmsXCjz8pUFVQ97Vo3jTKWreMeR98WOhSoChdeu2Q6Nara/z4uAti4Xz/vYH1td4NwuGHHx5PPfVUtGjRIk4//fQYMGBA7Lbbxg379O3b92v/FjmT+erOqqSkJEpKSmr0M+TXypUr4+WXX42eh3WLhx/+U3a9Z89u8Yc/1O5tabAl+Xzxkliw8JNo3qxpoUuBonPZdUOie+9ucfb/d2F89P78QpdDHVOsswH5st4NQqNGjeL++++Po48+OurVq1crF2/Tpk388pe/jL59+671/ZkzZ0anTp1q5VoU1s233BF3Trglpk9/JV742/T40cDTou1OO8a42+8udGlQNL744suY98FH2dcffvRxvDF7TpRu1yRKt2sSvxz/mzi8+8HRolnT+HD+x3HLuImxQ+l20TO1VwIQcXn50Pj+cT3j4jP/K75Y+kU0a1HVRC/919KoWG5mB77OejcIX3c70Ibo1KlTvPzyy+tsENyjvvm4775HolnTHeLK4RdFmzYt47V/vBl9jukX8+a5NQL+7bU33ooBF1yeff2z0bdHRMSxvXvGVZeeH2/NeTf+8NhfYsnSZdGiWdM4oOM+8fNrhkXjxtsUqmQoSsefcVxERNz+wOic9ZGDr49Hf/9YIUqijtnS//S53jspbwrPPvtsLFu2LL7//e+v9f1ly5bFtGnT4tBDD63Ree2kDBvGTsqwYeykDDVXzDspv1D2g7xd67sfPZC3a62vGg0p17ZDDjnkK99v3LhxjZsDAABgwxW0QQAAgGKzpQ8pb9RGaQAAwOZlgxqEu+++O7p27RplZWXx3nvvRUTEqFGj4uGHH67V4gAAIN+29I3SatwgjB07NoYOHRpHHnlkfP7557F69eqIiNh+++1j1KhRtV0fAACQRzVuEEaPHh133HFHDB8+PGc/hM6dO8ff//73Wi0OAADyrTKPRzGqcYMwd+7c6NChwxrrJSUlsWzZslopCgAAKIwaNwjt27ePmTNnrrH+2GOPxZ577lkbNQEAQMEkkcnbUYxq/JjTSy+9NAYNGhTLly+PJEnixRdfjHvuuSfKy8vjV7/61aaoEQAAyJMaNwhnnnlmrFq1Ki677LL44osv4pRTTokdd9wxbrnlljjppJM2RY0AAJA3lUmhKyisDdoo7Uc/+lH86Ec/ikWLFkVlZWW0bNmytusCAAAKYKN2Um7evHlt1QEAAEWhskhnA/Klxg1C+/btI5NZ9z+0d955Z6MKAgAACqfGDcKQIUNyXq9cuTJmzJgRf/rTn+LSSy+trboAAKAgivXpQvlS4wZh8ODBa13/5S9/GdOmTdvoggAAgMKp8T4I69K7d++4//77a+t0AABQEHZSriX/8z//E02bNq2t0wEAAAVQ41uMOnTokDOknCRJLFiwID755JO49dZba7U4AADINzMINdS3b9+c11tttVW0aNEiunfvHrvvvntt1QUAABRAjRqEVatWRbt27eKII46I1q1bb6qaAACgYIp1NiBfajSDUL9+/Tj33HOjoqJiU9UDAAAUUI2HlA888MCYMWPGpqgFAAAosBrPIJx33nlx8cUXxwcffBCdOnWKxo0b57y/zz771FpxAACQb1v6LUbr3SAMGDAgRo0aFSeeeGJERFx44YXZ9zKZTCRJEplMJlavXl37VQIAAHmx3g3CnXfeGTfccEPMnTt3U9YDAAAF5TGn6ylJkoiI2HnnnTdZMQAAQGHVaAYhvUEaAABsjiq38D/y1qhB+Pa3v/21TcKnn366UQUBAACFU6MG4eqrr47S0tJNVQsAABRcpRmE9XfSSSdFy5YtN1UtAABAga13g2D+AACALUFS6AIKbL13Uv73U4wAAIDN13onCJWVW/qecgAAbAm29D/1rneCAAAAbP5qNKQMAACbu8otfPZWggAAAGRJEAAAIGVLfzSPBAEAAMiSIAAAQIqnGAEAAFTTIAAAAFluMQIAgJTKLfsppxIEAADg/0gQAAAgpTK27AhBggAAAHXEhx9+GKeddlo0a9Ysttlmm9hvv/1i+vTptXoNCQIAAKQU60Zpn332WXTt2jV69OgRjz32WLRs2TLmzJkT22+/fa1eR4MAAAB1wI033hg77bRTTJgwIbvWrl27Wr+OW4wAACClMpO/o6KiIpYsWZJzVFRUrLWuRx55JDp37hzHH398tGzZMjp06BB33HFHrf/+GgQAACiQ8vLyKC0tzTnKy8vX+tl33nknxo4dG7vuums8/vjjcc4558SFF14Yd911V63WlEmSpFhvs9pg9RvuWOgSoE768qNnC10C1Eld9u5f6BKgzpk2v3j/mzNxx9Pydq2T3/n1GolBSUlJlJSUrPHZhg0bRufOneP555/Prl144YXx0ksvxV//+tdaq8kMAgAAFMi6moG1adOmTey55545a3vssUfcf//9tVqTBgEAAFKK9faarl27xptvvpmzNnv27Nh5551r9TpmEAAAoA646KKL4oUXXojrr78+3n777Zg0aVLcfvvtMWjQoFq9jgQBAABSKot0I+X9998/HnzwwRg2bFhcc8010b59+xg1alSceuqptXodDQIAANQRRx99dBx99NGb9BoaBAAASKksdAEFZgYBAADIkiAAAECKBAEAAKCaBAEAAFKSIn2KUb5IEAAAgCwNAgAAkOUWIwAASDGkDAAAUE2CAAAAKRIEAACAahIEAABISQpdQIFJEAAAgCwJAgAApFTaKA0AAKCKBAEAAFI8xQgAAKCaBAEAAFIkCAAAANUkCAAAkGIfBAAAgGoSBAAASLEPAgAAQDUJAgAApHiKEQAAQDUNAgAAkOUWIwAASPGYUwAAgGoSBAAASKncwjMEDQKQ1ajskEKXAHXSsmnjC10CQK3RIAAAQIrHnAIAAFSTIAAAQMqWPYEgQQAAAFIkCAAAkGIGAQAAoJoEAQAAUiozha6gsCQIAABAlgQBAABStvSdlCUIAABAlgQBAABStuz8QIIAAACkSBAAACDFPggAAADVJAgAAJDiKUYAAADVNAgAAECWW4wAACBly77BSIIAAACkSBAAACDFY04BAACqSRAAACDFY04BAACqSRAAACBly84PJAgAAECKBAEAAFI8xQgAAKCaBAEAAFKSLXwKQYIAAABkSRAAACDFDAIAAEA1DQIAAKRURpK3Y0OVl5dHJpOJIUOG1N4vXk2DAAAAdchLL70Ut99+e+yzzz6b5PwaBAAASEnyeNTU0qVL49RTT4077rgjdthhhw38Db+aBgEAAAqkoqIilixZknNUVFSs8/ODBg2Ko446Knr27LnJatIgAABAgZSXl0dpaWnOUV5evtbP/u53v4vp06ev8/3a4jGnAACQsjHDwzU1bNiwGDp0aM5aSUnJGp97//33Y/DgwfHnP/85tt56601akwYBAAAKpKSkZK0NwX+aPn16LFy4MDp16pRdW716dTzzzDMxZsyYqKioiHr16tVKTRoEAABIKcaN0g477LD4+9//nrN25plnxu677x6XX355rTUHERoEAAAoek2aNIm99torZ61x48bRrFmzNdY3lgYBAABSkjzOIBQjDQIAANRBTz/99CY5rwYBAABSinEGIZ/sgwAAAGRJEAAAIGVLn0GQIAAAAFkSBAAASDGDAAAAUE2CAAAAKZWJGQQAAICIkCAAAECOLTs/kCAAAAApEgQAAEip3MIzBAkCAACQJUEAAIAUOykDAABU0yAAAABZbjECAICUykIXUGASBAAAIEuCAAAAKR5zCgAAUE2CAAAAKR5zCgAAUE2CAAAAKZ5iBAAAUE2CAAAAKUliBgEAACAiJAgAAJDDPggAAADVJAgAAJDiKUYAAADVJAgAAJBiJ2UAAIBqEgQAAEjxFCMAAIBqGgQAACDLLUYAAJCSJG4xAgAAiAgJAgAA5LBRGgAAQDUJAgAApNgoDQAAoJoEAQAAUmyUBnlyztn94603/xpLl8yJv73wWBzc9YBClwR1gu8OfLVps96O828YF4f9+MrY5/gL48kXX13nZ68Z97vY5/gL4+7/fSqPFULdokEgL44//pi46Rcjo/yG/47OBxwRU6e+GI/+4Tex005lhS4NiprvDny9LytWxG477xjDBh7/lZ978sVX4+9vvRctdyjNU2XUVUmS5O0oRhoE8uKiwT+K8RN+F+Mn3BNvvPF2XHzJiHj/g4/inLNPL3RpUNR8d+DrHdJhz7jg5KOj54H7rvMzH//z87j+1/dF+eDTo379enmsDuoeDQKbXIMGDaJjx31i8hNTctYnT54SXb7buUBVQfHz3YHaUVlZGf81+u4445jDYped2hS6HOqAykjydhQjDQKbXPPmTaN+/fqx8ONFOesLFy6KVq1bFqgqKH6+O1A7xj/8RNSvt1WceuShhS4F6oSCNwhffvllTJ06NWbNmrXGe8uXL4+77rrrK3++oqIilixZknMU6/1cW7r//PeSyWT8u4L14LsDG27WnHnx2/+dEj8ddFpkMplCl0MdkeTxf8WooA3C7NmzY4899ohu3brF3nvvHd27d4/58+dn31+8eHGceeaZX3mO8vLyKC0tzTmSyn9t6tKpgUWLPo1Vq1ZFq9YtctZbtGgWCz/+pEBVQfHz3YGNN/2NOfHpkqVxxLkjosOJQ6LDiUPio08+jV/c+VB8/7yRhS4PilJBG4TLL7889t5771i4cGG8+eabsd1220XXrl1j3rx5632OYcOGxeLFi3OOzFZNNmHV1NTKlSvj5ZdfjZ6HdctZ79mzW/z1hWkFqgqKn+8ObLw+3Q6I//n55fH7/3dZ9mi5Q2mcccxhMXb4uYUujyJVmSR5O4pRQTdKe/755+OJJ56I5s2bR/PmzeORRx6JQYMGxSGHHBJPPfVUNG7c+GvPUVJSEiUlJTlrIsTic/Mtd8SdE26J6dNfiRf+Nj1+NPC0aLvTjjHu9rsLXRoUNd8d+HpffFkR8xb8X6r24cJ/xhtzP4jSbbeJNi2axvZNcv88Ub9+vWi2Q5Nov2OrfJcKdUJBG4Qvv/wy6tfPLeGXv/xlbLXVVnHooYfGpEmTClQZte2++x6JZk13iCuHXxRt2rSM1/7xZvQ5pl/Mm/dhoUuDoua7A1/vH+/Mi4EjR2df/787H4yIiGMOPSCuPf+0QpVFHVacf6+fP5mkgJNuBxxwQFxwwQXRr1+/Nd47//zz47e//W0sWbIkVq9eXaPz1m+4Y22VCABfa9m08YUuAeqckn2OKHQJ63TIjofl7VrPfviXvF1rfRV0BuG4446Le+65Z63vjRkzJk4++WRP6gAAgDwqaIKwqUgQAMgnCQLUXDEnCF13/F7ervXch0/m7Vrrq+D7IAAAAMWjoEPKAABQbCq38DFlCQIAAJAlQQAAgJTNcES3RiQIAABAlgQBAABSzCAAAABU0yAAAEBKksf/1UR5eXnsv//+0aRJk2jZsmX07ds33nzzzVr//TUIAABQB0yZMiUGDRoUL7zwQkyePDlWrVoVvXr1imXLltXqdcwgAABASrE+xehPf/pTzusJEyZEy5YtY/r06dGtW7dau44GAQAACqSioiIqKipy1kpKSqKkpORrf3bx4sUREdG0adNarcktRgAAkFIZSd6O8vLyKC0tzTnKy8u/tsYkSWLo0KFx8MEHx1577VWrv78EAQAACmTYsGExdOjQnLX1SQ/OP//8ePXVV2Pq1Km1XpMGAQAAUvI5g7C+txOlXXDBBfHII4/EM888E9/4xjdqvSYNAgAA1AFJksQFF1wQDz74YDz99NPRvn37TXIdDQIAAKQU607KgwYNikmTJsXDDz8cTZo0iQULFkRERGlpaTRq1KjWrmNIGQAA6oCxY8fG4sWLo3v37tGmTZvsce+999bqdSQIAACQUtMdjvMlX7MREgQAACBLgwAAAGS5xQgAAFIq8/iY02IkQQAAALIkCAAAkFKsQ8r5IkEAAACyJAgAAJBiBgEAAKCaBAEAAFLMIAAAAFSTIAAAQIoZBAAAgGoSBAAASDGDAAAAUE2CAAAAKWYQAAAAqkkQAAAgxQwCAABANQkCAACkJElloUsoKAkCAACQpUEAAACy3GIEAAAplYaUAQAAqkgQAAAgJbFRGgAAQBUJAgAApJhBAAAAqCZBAACAFDMIAAAA1SQIAACQUilBAAAAqCJBAACAlMRTjAAAAKpIEAAAIMVTjAAAAKpJEAAAIMVOygAAANUkCAAAkGIGAQAAoJoEAQAAUuykDAAAUE2DAAAAZLnFCAAAUgwpAwAAVJMgAABAio3SAAAAqkkQAAAgxQwCAABANQkCAACk2CgNAACgmgQBAABSEk8xAgAAqCJBAACAFDMIAAAA1SQIAACQYh8EAACAahIEAABI8RQjAACAahIEAABIMYMAAABQTYMAAAB1yK233hrt27ePrbfeOjp16hTPPvtsrZ5fgwAAAClJkuTtqKl77703hgwZEsOHD48ZM2bEIYccEr1794558+bV2u+fSTbDm6zqN9yx0CUAsAVZNm18oUuAOqdknyMKXcI6NcjjnyVXrviwRp8/8MADo2PHjjF27Njs2h577BF9+/aN8vLyWqlJggAAAClJHo+aWLFiRUyfPj169eqVs96rV694/vnna/prrpOnGAEAQIFUVFRERUVFzlpJSUmUlJSs8dlFixbF6tWro1WrVjnrrVq1igULFtRaTZtlg7CqhlEN+VNRURHl5eUxbNiwtf4fH1iT7w1sGN8dNlQ+/yw5cuTIuPrqq3PWRowYESNHjlznz2QymZzXSZKssbYxNssZBIrXkiVLorS0NBYvXhzbbbddocuBOsH3BjaM7w51QU0ShBUrVsQ222wT9913Xxx33HHZ9cGDB8fMmTNjypQptVKTGQQAACiQkpKS2G677XKOdSVeDRs2jE6dOsXkyZNz1idPnhwHHXRQrdW0Wd5iBAAAm6OhQ4dGv379onPnztGlS5e4/fbbY968eXHOOefU2jU0CAAAUEeceOKJ8c9//jOuueaamD9/fuy1117xxz/+MXbeeedau4YGgbwqKSmJESNGGBaDGvC9gQ3ju8Pm6rzzzovzzjtvk53fkDIAAJBlSBkAAMjSIAAAAFkaBAAAIEuDAAAAZGkQyJtbb7012rdvH1tvvXV06tQpnn322UKXBEXtmWeeiT59+kRZWVlkMpl46KGHCl0S1Anl5eWx//77R5MmTaJly5bRt2/fePPNNwtdFtQZGgTy4t57740hQ4bE8OHDY8aMGXHIIYdE7969Y968eYUuDYrWsmXLYt99940xY8YUuhSoU6ZMmRKDBg2KF154ISZPnhyrVq2KXr16xbJlywpdGtQJHnNKXhx44IHRsWPHGDt2bHZtjz32iL59+0Z5eXkBK4O6IZPJxIMPPhh9+/YtdClQ53zyySfRsmXLmDJlSnTr1q3Q5UDRkyCwya1YsSKmT58evXr1ylnv1atXPP/88wWqCoAtxeLFiyMiomnTpgWuBOoGDQKb3KJFi2L16tXRqlWrnPVWrVrFggULClQVAFuCJEli6NChcfDBB8dee+1V6HKgTqhf6ALYcmQymZzXSZKssQYAten888+PV199NaZOnVroUqDO0CCwyTVv3jzq1au3RlqwcOHCNVIFAKgtF1xwQTzyyCPxzDPPxDe+8Y1ClwN1hluM2OQaNmwYnTp1ismTJ+esT548OQ466KACVQXA5ipJkjj//PPjgQceiCeffDLat29f6JKgTpEgkBdDhw6Nfv36RefOnaNLly5x++23x7x58+Kcc84pdGlQtJYuXRpvv/129vXcuXNj5syZ0bRp02jbtm0BK4PiNmjQoJg0aVI8/PDD0aRJk2yCXVpaGo0aNSpwdVD8POaUvLn11lvjZz/7WcyfPz/22muvuPnmmz1uDr7C008/HT169FhjvX///jFx4sT8FwR1xLrm2yZMmBBnnHFGfouBOkiDAAAAZJlBAAAAsjQIAABAlgYBAADI0iAAAABZGgQAACBLgwAAAGRpEAAAgCwNAsBGGDlyZOy3337Z12eccUb07ds373W8++67kclkYubMmZv0Ou3atYtRo0Zt0msAUFgaBGCzc8YZZ0Qmk4lMJhMNGjSIb37zm3HJJZfEsmXLNvm1b7nllvXe5Thff6iPiNh7773jrLPOWut799xzTzRo0CA+/vjjTV4HAMVPgwBslr7//e/H/Pnz45133olrr702br311rjkkkvW+tmVK1fW2nVLS0tj++23r7Xz1ZaBAwfG73//+/jiiy/WeG/8+PFx9NFHR6tWrQpQGQDFRoMAbJZKSkqidevWsdNOO8Upp5wSp556ajz00EMR8X+3BY0fPz6++c1vRklJSSRJEosXL44f//jH0bJly9huu+3ie9/7Xrzyyis5573hhhuiVatW0aRJkxg4cGAsX7485/3/vMWosrIybrzxxthll12ipKQk2rZtG9ddd11ERLRv3z4iIjp06BCZTCa6d++e/bkJEybEHnvsEVtvvXXsvvvuceutt+Zc58UXX4wOHTrE1ltvHZ07d44ZM2Z85T+Pfv36RUVFRdx333056/PmzYsnn3wyBg4cGHPmzIljjz02WrVqFdtuu23sv//+8cQTT6zznGtLQD7//PPIZDLx9NNPZ9dmzZoVRx55ZGy77bbRqlWr6NevXyxatOgr6wWgcDQIwBahUaNGOUnB22+/Hb///e/j/vvvz/4B96ijjooFCxbEH//4x5g+fXp07NgxDjvssPj0008jIuL3v/99jBgxIq677rqYNm1atGnTZo0/uP+nYcOGxY033hhXXXVVzJo1KyZNmpT9m/oXX3wxIiKeeOKJmD9/fjzwwAMREXHHHXfE8OHD47rrrovXX389rr/++rjqqqvizjvvjIiIZcuWxdFHHx277bZbTJ8+PUaOHLnOdOTfmjVrFscee2xMmDAhZ33ChAnRqlWr6N27dyxdujSOPPLIeOKJJ2LGjBlxxBFHRJ8+fWLevHnr+U95TfPnz49DDz009ttvv5g2bVr86U9/io8//jhOOOGEDT4nAJtYArCZ6d+/f3LsscdmX//tb39LmjVrlpxwwglJkiTJiBEjkgYNGiQLFy7MfuYvf/lLst122yXLly/POde3vvWtZNy4cUmSJEmXLl2Sc845J+f9Aw88MNl3333Xeu0lS5YkJSUlyR133LHWOufOnZtERDJjxoyc9Z122imZNGlSztpPf/rTpEuXLkmSJMm4ceOSpk2bJsuWLcu+P3bs2LWeK+2xxx5LMplMMmfOnCRJkqSysjJp165dMmzYsHX+zJ577pmMHj06+3rnnXdObr755nXW/9lnnyURkTz11FNJkiTJVVddlfTq1SvnnO+//34SEcmbb765zusCUDgSBGCz9Oijj8a2224bW2+9dXTp0iW6desWo0ePzr6/8847R4sWLbKvp0+fHkuXLo1mzZrFtttumz3mzp0bc+bMiYiI119/Pbp06ZJznf98nfb6669HRUVFHHbYYetd9yeffBLvv/9+DBw4MKeOa6+9NqeOfffdN7bZZpv1quPfevXqFd/4xjeyKcKTTz4Z7777bpx55pkRUZVMXHbZZbHnnnvG9ttvH9tuu2288cYbG5UgTJ8+PZ566qmc32X33XePiMj+PgAUl/qFLgBgU+jRo0eMHTs2GjRoEGVlZdGgQYOc9xs3bpzzurKyMtq0aZNz7/y/bejQcaNGjWr8M5WVlRFRdZvRgQcemPNevXr1IiIiSZINqmerrbaKM844IyZOnBhXX311TJgwIbp16xa77rprRERceuml8fjjj8fPf/7z2GWXXaJRo0bxwx/+MFasWLHO8/1nPf858F1ZWRl9+vSJG2+8cY2fb9OmzQb9HgBsWhoEYLPUuHHj2GWXXdb78x07dowFCxZE/fr1o127dmv9zB577BEvvPBCnH766dm1F154YZ3n3HXXXaNRo0bxl7/8Za2PGG3YsGFERKxevTq71qpVq9hxxx3jnXfeiVNPPXWt591zzz3j7rvvji+//DLbhHxVHWlnnnlmXHvttfHAAw/EAw88ELfddlv2vWeffTbOOOOMOO644yIiYunSpfHuu++u81z/TmDmz58fHTp0iIhY45GtHTt2jPvvvz/atWsX9ev7Tw5AXeAWI4CI6NmzZ3Tp0iX69u0bjz/+eLz77rvx/PPPx5VXXhnTpk2LiIjBgwfH+PHjY/z48TF79uwYMWJE/OMf/1jnObfeeuu4/PLL47LLLou77ror5syZEy+88EL8+te/joiIli1bRqNGjbKDu4sXL46IqqcslZeXxy233BKzZ8+Ov//97zFhwoS46aabIiLilFNOia222ioGDhwYs2bNij/+8Y/x85//fL1+z/bt28f3vve9+PGPfxwNGjSIH/7wh9n3dtlll3jggQdi5syZ8corr8Qpp5ySTTTWplGjRvHd7343brjhhpg1a1Y888wzceWVV+Z8ZtCgQfHpp5/GySefHC+++GK888478ec//zkGDBiQ0xgBUDw0CAARkclk4o9//GN069YtBgwYEN/+9rfjpJNOinfffTf71KETTzwxfvKTn8Tll18enTp1ivfeey/OPffcrzzvVVddFRdffHH85Cc/iT322CNOPPHEWLhwYURE1K9fP/77v/87xo0bF2VlZXHsscdGRMRZZ50Vv/rVr2LixImx9957x6GHHhoTJ07MPhZ12223jT/84Q8xa9as6NChQwwfPnytt/Csy8CBA+Ozzz6Lk046KWeO4eabb44ddtghDjrooOjTp08cccQR0bFjx6881/jx42PlypXRuXPnGDx4cFx77bU575eVlcVzzz0Xq1evjiOOOCL22muvGDx4cJSWlmZvUQKguGSSDb2ZFQAA2Oz46xsAACBLgwAAAGRpEAAAgCwNAgAAkKVBAAAAsjQIAABAlgYBAADI0iAAAABZGgQAACBLgwAAAGRpEAAAgCwNAgAAkPX/A9SU+YpTe16CAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline \n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "plt.figure(figsize = (10,7))\n",
+    "sns.heatmap(clfx, annot = True)\n",
+    "plt.xlabel(\"Predicted Value\")\n",
+    "plt.ylabel(\"True Value\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0697431a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "From the confusion matrix, it can be seen that the model built predicted the values for 'setosa' correctly, predicted the values\n",
+    "for 'versicolor' correctly also, but predicted 14 values for 'virginica' correctly and 2 values for 'virginica' wrongly. It confused \n",
+    "it for 'versicolor'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a3fd71ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['DESCR',\n",
+       " 'data',\n",
+       " 'data_filename',\n",
+       " 'data_module',\n",
+       " 'feature_names',\n",
+       " 'frame',\n",
+       " 'target',\n",
+       " 'target_filename']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes = datasets.load_diabetes()\n",
+    "dir(diabetes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "04397e9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = diabetes.data\n",
+    "y = diabetes.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "44a0b072",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "7f2c960b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=30)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(n_estimators=30)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diab_model = RandomForestClassifier(n_estimators= 30)\n",
+    "diab_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "fa3f2644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diab_model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "9be51f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes.feature_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "6e7d0de1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor()"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "model2 = RandomForestRegressor(n_estimators= 100)\n",
+    "model2.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "5745eb13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3072018707051737"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "93eb9ab3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.038076</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.061696</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>-0.044223</td>\n",
+       "      <td>-0.034821</td>\n",
+       "      <td>-0.043401</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.019907</td>\n",
+       "      <td>-0.017646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.001882</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.051474</td>\n",
+       "      <td>-0.026328</td>\n",
+       "      <td>-0.008449</td>\n",
+       "      <td>-0.019163</td>\n",
+       "      <td>0.074412</td>\n",
+       "      <td>-0.039493</td>\n",
+       "      <td>-0.068332</td>\n",
+       "      <td>-0.092204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.085299</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.044451</td>\n",
+       "      <td>-0.005670</td>\n",
+       "      <td>-0.045599</td>\n",
+       "      <td>-0.034194</td>\n",
+       "      <td>-0.032356</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.002861</td>\n",
+       "      <td>-0.025930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.089063</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.011595</td>\n",
+       "      <td>-0.036656</td>\n",
+       "      <td>0.012191</td>\n",
+       "      <td>0.024991</td>\n",
+       "      <td>-0.036038</td>\n",
+       "      <td>0.034309</td>\n",
+       "      <td>0.022688</td>\n",
+       "      <td>-0.009362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.005383</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.036385</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>0.003935</td>\n",
+       "      <td>0.015596</td>\n",
+       "      <td>0.008142</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>-0.031988</td>\n",
+       "      <td>-0.046641</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4         5         6  \\\n",
+       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
+       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
+       "2  0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
+       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
+       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
+       "\n",
+       "          7         8         9  \n",
+       "0 -0.002592  0.019907 -0.017646  \n",
+       "1 -0.039493 -0.068332 -0.092204  \n",
+       "2 -0.002592  0.002861 -0.025930  \n",
+       "3  0.034309  0.022688 -0.009362  \n",
+       "4 -0.002592 -0.031988 -0.046641  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(diabetes.data)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "883a01cc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/RandomForest- Deborah O..ipynb b/RandomForest- Deborah O..ipynb
new file mode 100644
index 0000000..5457bcc
--- /dev/null
+++ b/RandomForest- Deborah O..ipynb	
@@ -0,0 +1,778 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c6c856ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2444b6a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris = datasets.load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "target_names = iris.target_names\n",
+    "target_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "1bd2e32b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1    2    3\n",
+       "0  5.1  3.5  1.4  0.2\n",
+       "1  4.9  3.0  1.4  0.2\n",
+       "2  4.7  3.2  1.3  0.2\n",
+       "3  4.6  3.1  1.5  0.2\n",
+       "4  5.0  3.6  1.4  0.2"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(iris.data)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "084db56a",
+   "metadata": {},
+   "source": [
+    "# splitting the dataset to training and testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "86c6e0aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cadc1bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e89d11",
+   "metadata": {},
+   "source": [
+    "# Building the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fe6ebfa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=50)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(n_estimators=50)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "model = RandomForestClassifier(n_estimators= 50)\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "17dfd379",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 2, 0, 0, 2, 0, 2, 2, 1, 1,\n",
+       "       1, 2, 2, 2, 1, 0, 1, 0, 2, 1, 1, 1, 2, 0, 1, 2, 2, 2, 2, 0, 2, 0,\n",
+       "       1])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "95e06ef6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6.4, 2.9, 4.3, 1.3],\n",
+       "       [5.5, 2.6, 4.4, 1.2],\n",
+       "       [5.2, 4.1, 1.5, 0.1],\n",
+       "       [5.9, 3. , 4.2, 1.5],\n",
+       "       [4.4, 3.2, 1.3, 0.2],\n",
+       "       [5.6, 2.9, 3.6, 1.3],\n",
+       "       [6. , 2.9, 4.5, 1.5],\n",
+       "       [5.2, 3.4, 1.4, 0.2],\n",
+       "       [5.8, 4. , 1.2, 0.2],\n",
+       "       [5.5, 2.3, 4. , 1.3],\n",
+       "       [5.1, 3.8, 1.5, 0.3],\n",
+       "       [6. , 2.7, 5.1, 1.6],\n",
+       "       [4.3, 3. , 1.1, 0.1],\n",
+       "       [5.7, 2.5, 5. , 2. ],\n",
+       "       [4.6, 3.2, 1.4, 0.2],\n",
+       "       [5.4, 3.9, 1.7, 0.4],\n",
+       "       [6.8, 3.2, 5.9, 2.3],\n",
+       "       [4.6, 3.6, 1. , 0.2],\n",
+       "       [5.9, 3. , 5.1, 1.8],\n",
+       "       [5.8, 2.8, 5.1, 2.4],\n",
+       "       [5.5, 2.4, 3.7, 1. ],\n",
+       "       [6.7, 3. , 5. , 1.7],\n",
+       "       [6. , 3.4, 4.5, 1.6],\n",
+       "       [6.5, 3. , 5.5, 1.8],\n",
+       "       [6.9, 3.2, 5.7, 2.3],\n",
+       "       [7.7, 2.6, 6.9, 2.3],\n",
+       "       [6. , 2.2, 4. , 1. ],\n",
+       "       [4.4, 2.9, 1.4, 0.2],\n",
+       "       [5.8, 2.7, 3.9, 1.2],\n",
+       "       [4.8, 3.4, 1.9, 0.2],\n",
+       "       [6.1, 2.6, 5.6, 1.4],\n",
+       "       [6.1, 2.9, 4.7, 1.4],\n",
+       "       [6.2, 2.2, 4.5, 1.5],\n",
+       "       [5.8, 2.6, 4. , 1.2],\n",
+       "       [6.3, 2.8, 5.1, 1.5],\n",
+       "       [5.5, 4.2, 1.4, 0.2],\n",
+       "       [6.1, 2.8, 4.7, 1.2],\n",
+       "       [7.1, 3. , 5.9, 2.1],\n",
+       "       [6.7, 3.3, 5.7, 2.5],\n",
+       "       [7.6, 3. , 6.6, 2.1],\n",
+       "       [6.5, 3.2, 5.1, 2. ],\n",
+       "       [5.4, 3.4, 1.5, 0.4],\n",
+       "       [7.7, 2.8, 6.7, 2. ],\n",
+       "       [5.1, 3.8, 1.9, 0.4],\n",
+       "       [6.4, 3.2, 4.5, 1.5]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c48d3056",
+   "metadata": {},
+   "source": [
+    "# Checking the model accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bda0fa81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9555555555555556"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eab165b",
+   "metadata": {},
+   "source": [
+    "# predicting using the model built"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d2422899",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 2, 0, 2, 0, 0, 2, 0, 2, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 1, 0, 1, 0, 2, 1, 1, 1, 2, 0, 1, 2, 2, 2, 2, 0, 2, 0,\n",
+       "       1])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predicted_value = model.predict(X_test)\n",
+    "predicted_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28914d28",
+   "metadata": {},
+   "source": [
+    "# Validating the model using confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0062630e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[14,  0,  0],\n",
+       "       [ 0, 15,  2],\n",
+       "       [ 0,  0, 14]], dtype=int64)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "clfx = confusion_matrix(y_test, predicted_value)\n",
+    "clfx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f479555a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(45, 4)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffbcd935",
+   "metadata": {},
+   "source": [
+    "# Visualising the confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c5ef3e40",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline \n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "plt.figure(figsize = (10,7))\n",
+    "sns.heatmap(clfx, annot = True)\n",
+    "plt.xlabel(\"Predicted Value\")\n",
+    "plt.ylabel(\"True Value\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e610386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "From the confusion matrix, it can be seen that the model built predicted the values for 'setosa' correctly, predicted the values\n",
+    "for 'versicolor' correctly also, but predicted 14 values for 'virginica' correctly and 2 values for 'virginica' wrongly. It confused \n",
+    "it for 'versicolor'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e5679084",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['DESCR',\n",
+       " 'data',\n",
+       " 'data_filename',\n",
+       " 'data_module',\n",
+       " 'feature_names',\n",
+       " 'frame',\n",
+       " 'target',\n",
+       " 'target_filename']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes = datasets.load_diabetes()\n",
+    "dir(diabetes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d6cd8827",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = diabetes.data\n",
+    "y = diabetes.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "19a23fca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "b007be23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=30)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(n_estimators=30)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diab_model = RandomForestClassifier(n_estimators= 30)\n",
+    "diab_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "22683910",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diab_model.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "9eed31da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes.feature_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "539dc1a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor()"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "model2 = RandomForestRegressor(n_estimators= 100)\n",
+    "model2.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "ae5a552c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3072018707051737"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model2.score(X_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "09fb4676",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.038076</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.061696</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>-0.044223</td>\n",
+       "      <td>-0.034821</td>\n",
+       "      <td>-0.043401</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.019907</td>\n",
+       "      <td>-0.017646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.001882</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.051474</td>\n",
+       "      <td>-0.026328</td>\n",
+       "      <td>-0.008449</td>\n",
+       "      <td>-0.019163</td>\n",
+       "      <td>0.074412</td>\n",
+       "      <td>-0.039493</td>\n",
+       "      <td>-0.068332</td>\n",
+       "      <td>-0.092204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.085299</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.044451</td>\n",
+       "      <td>-0.005670</td>\n",
+       "      <td>-0.045599</td>\n",
+       "      <td>-0.034194</td>\n",
+       "      <td>-0.032356</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.002861</td>\n",
+       "      <td>-0.025930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.089063</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.011595</td>\n",
+       "      <td>-0.036656</td>\n",
+       "      <td>0.012191</td>\n",
+       "      <td>0.024991</td>\n",
+       "      <td>-0.036038</td>\n",
+       "      <td>0.034309</td>\n",
+       "      <td>0.022688</td>\n",
+       "      <td>-0.009362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.005383</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.036385</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>0.003935</td>\n",
+       "      <td>0.015596</td>\n",
+       "      <td>0.008142</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>-0.031988</td>\n",
+       "      <td>-0.046641</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4         5         6  \\\n",
+       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
+       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
+       "2  0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
+       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
+       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
+       "\n",
+       "          7         8         9  \n",
+       "0 -0.002592  0.019907 -0.017646  \n",
+       "1 -0.039493 -0.068332 -0.092204  \n",
+       "2 -0.002592  0.002861 -0.025930  \n",
+       "3  0.034309  0.022688 -0.009362  \n",
+       "4 -0.002592 -0.031988 -0.046641  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(diabetes.data)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "760f6747",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}