bl.en.u4cse21199 lab6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8f2d8be7-a553-4ea9-9898-b5978ca73c30",
   "metadata": {},
   "source": [
    "Name:- Subodh Narayan Sah\n",
    "Reg:-BL.EN.U4CSE21199\n",
    "Lab:-06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7f3d3bb6-14f0-4c20-ba4a-3ae6b1811f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "55ada751-d02b-44f5-bf5c-c7044b894ba1",
   "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",
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       "    }\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>UNIXTime</th>\n",
       "      <th>Data</th>\n",
       "      <th>Time</th>\n",
       "      <th>Radiation</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindDirection(Degrees)</th>\n",
       "      <th>Speed</th>\n",
       "      <th>TimeSunRise</th>\n",
       "      <th>TimeSunSet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1475229326</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:55:26</td>\n",
       "      <td>1.21</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>59</td>\n",
       "      <td>177.39</td>\n",
       "      <td>5.62</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1475229023</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:50:23</td>\n",
       "      <td>1.21</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>58</td>\n",
       "      <td>176.78</td>\n",
       "      <td>3.37</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1475228726</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:45:26</td>\n",
       "      <td>1.23</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>57</td>\n",
       "      <td>158.75</td>\n",
       "      <td>3.37</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1475228421</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:40:21</td>\n",
       "      <td>1.21</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>60</td>\n",
       "      <td>137.71</td>\n",
       "      <td>3.37</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1475228124</td>\n",
       "      <td>9/29/2016 0:00</td>\n",
       "      <td>23:35:24</td>\n",
       "      <td>1.17</td>\n",
       "      <td>48</td>\n",
       "      <td>30.46</td>\n",
       "      <td>62</td>\n",
       "      <td>104.95</td>\n",
       "      <td>5.62</td>\n",
       "      <td>6:13:00</td>\n",
       "      <td>18:13:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     UNIXTime            Data      Time  Radiation  Temperature  Pressure  \\\n",
       "0  1475229326  9/29/2016 0:00  23:55:26       1.21           48     30.46   \n",
       "1  1475229023  9/29/2016 0:00  23:50:23       1.21           48     30.46   \n",
       "2  1475228726  9/29/2016 0:00  23:45:26       1.23           48     30.46   \n",
       "3  1475228421  9/29/2016 0:00  23:40:21       1.21           48     30.46   \n",
       "4  1475228124  9/29/2016 0:00  23:35:24       1.17           48     30.46   \n",
       "\n",
       "   Humidity  WindDirection(Degrees)  Speed TimeSunRise TimeSunSet  \n",
       "0        59                  177.39   5.62     6:13:00   18:13:00  \n",
       "1        58                  176.78   3.37     6:13:00   18:13:00  \n",
       "2        57                  158.75   3.37     6:13:00   18:13:00  \n",
       "3        60                  137.71   3.37     6:13:00   18:13:00  \n",
       "4        62                  104.95   5.62     6:13:00   18:13:00  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"SolarPrediction.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7968bb-717d-4ab4-a513-00c27dd1bec1",
   "metadata": {},
   "source": [
    "A1. Take any 2 features from your project having has real numeric values. Make a scatter plot of the \n",
    "data and observe the pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ef174858-3d1b-42b8-879b-73a3321d7d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter Plot between Temperature and Humidity')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[\"Temperature\"],data[\"Humidity\"])\n",
    "plt.xlabel(\"Temperature\")\n",
    "plt.ylabel(\"Humidity\")\n",
    "plt.title(\"Scatter Plot between Temperature and Humidity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d37b4d-8f77-44e1-b36d-5b905eb6db60",
   "metadata": {},
   "source": [
    "#A2. Create a linear regression model on this data. Consider using one feature as independent variable while the other as dependent variable \n",
    "#(you may also round this number to integer). After the model is created, calculate the mean square error by predicting the values from the model. \n",
    "#Refer site: https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "20f34220-f9c9-4e63-afb4-8a2ffaae5273",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [37.45003797]\n",
      "Mean squared error: 4797.94\n",
      "Coefficient of determination: -26075784.65\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load your dataset from 'data.csv'\n",
    "\n",
    "# Specify independent and dependent variables\n",
    "X = data[['Temperature']]  # Independent variables\n",
    "y = data['Radiation']  # Dependent variable\n",
    "\n",
    "# Split the data into training/testing sets\n",
    "X_train = X[:-20]\n",
    "X_test = X[-20:]\n",
    "\n",
    "y_train = y[:-20]\n",
    "y_test = y[-20:]\n",
    "\n",
    "# Create a linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "regr.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions using the testing set\n",
    "y_pred = regr.predict(X_test)\n",
    "\n",
    "# The coefficients\n",
    "print(\"Coefficients: \\n\", regr.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\" % mean_squared_error(y_test, y_pred))\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print(\"Coefficient of determination: %.2f\" % r2_score(y_test, y_pred))\n",
    "# Plot outputs\n",
    "plt.scatter(X_test['Temperature'], y_test, color=\"black\", label=\"Actual\")\n",
    "plt.plot(X_test['Temperature'], y_pred, color=\"blue\", linewidth=3, label=\"Predicted\")\n",
    "plt.legend()\n",
    "plt.xlabel('Temperature (temp)')\n",
    "plt.ylabel('Radiation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "869619cc-0e6e-4162-a851-addf055b8bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#A4. Using the training set available for your project, train a logistic regression classifier. Use this classifier to evaluate your test set accuracy. \n",
    "#Study the various parameters associated with logistic regression model and the role they play in the model training.[NOTE]: For Logistic regression,\n",
    "#use bi-class classification problem. If your data has multiple classes, take any two classes. If your data has value prediction problem\n",
    "#(Ex: stock price or car value prediction), convert it into a bi-class classification problem and fit a logistic regressor.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e4882418-7955-4290-841c-89b479ffd705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 80.00%\n",
      "Confusion Matrix:\n",
      "[[12  5]\n",
      " [ 3 20]]\n"
     ]
    }
   ],
   "source": [
    "import statistics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "# Calculate the threshold for binary classification\n",
    "threshold = statistics.mean(data['Radiation'])\n",
    "\n",
    "# Create a binary label based on the threshold\n",
    "data['Binary_Label'] = (data['Radiation'] >= threshold).astype(int)\n",
    "\n",
    "# Extract features and labels for the first 200 rows\n",
    "feat1 = data['Humidity'].head(200).values.reshape(-1, 1)\n",
    "label = data['Binary_Label'].head(200).values\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(feat1, label, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Create and train a logistic regression model\n",
    "model = LogisticRegression(C=1.0, solver='liblinear')\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))\n",
    "\n",
    "# Calculate the confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2a128f19-0429-4e2e-8ecf-78e7caaa11e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression Tree Metrics:\n",
      "MAE: 101.63\n",
      "MSE: 41066.44\n",
      "R-squared: 0.59\n",
      "\n",
      "k-NN Regressor Metrics:\n",
      "MAE: 108.26\n",
      "MSE: 36532.67\n",
      "R-squared: 0.63\n"
     ]
    }
   ],
   "source": [
    "#A5. Use a Regression Tree and k-NN Regressor for value prediction. Use the data employed for A1.\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# Assuming your dataset has features (X) and a target variable (y)\n",
    "X = data[['Temperature', 'Pressure', 'Humidity']]  # Replace with your actual feature column names\n",
    "y = data['Radiation']  # Replace with your actual target variable column name\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Regression Tree\n",
    "tree_regressor = DecisionTreeRegressor(random_state=42)\n",
    "tree_regressor.fit(X_train, y_train)\n",
    "y_tree_pred = tree_regressor.predict(X_test)\n",
    "\n",
    "# k-NN Regressor\n",
    "k = 5  # You can adjust k as needed\n",
    "knn_regressor = KNeighborsRegressor(n_neighbors=k)\n",
    "knn_regressor.fit(X_train, y_train)\n",
    "y_knn_pred = knn_regressor.predict(X_test)\n",
    "\n",
    "# Evaluate the models\n",
    "tree_mae = mean_absolute_error(y_test, y_tree_pred)\n",
    "tree_mse = mean_squared_error(y_test, y_tree_pred)\n",
    "tree_r2 = r2_score(y_test, y_tree_pred)\n",
    "\n",
    "knn_mae = mean_absolute_error(y_test, y_knn_pred)\n",
    "knn_mse = mean_squared_error(y_test, y_knn_pred)\n",
    "knn_r2 = r2_score(y_test, y_knn_pred)\n",
    "\n",
    "print(\"Regression Tree Metrics:\")\n",
    "print(f\"MAE: {tree_mae:.2f}\")\n",
    "print(f\"MSE: {tree_mse:.2f}\")\n",
    "print(f\"R-squared: {tree_r2:.2f}\")\n",
    "\n",
    "print(\"\\nk-NN Regressor Metrics:\")\n",
    "print(f\"MAE: {knn_mae:.2f}\")\n",
    "print(f\"MSE: {knn_mse:.2f}\")\n",
    "print(f\"R-squared: {knn_r2:.2f}\")\n"
   ]
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