diff --git a/student.ipynb b/student.ipynb
index d3bb34af..43384241 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -7,21 +7,1702 @@
     "## Final Project Submission\n",
     "\n",
     "Please fill out:\n",
-    "* Student name: \n",
-    "* Student pace: self paced / part time / full time\n",
+    "* Student names: CALVIN OMWEGA,COLLINS BIWOTT,INGAVI KILAVUKA,MERCY KIRAGU\n",
+    "                 \n",
+    "* Student pace: Full time-Hybrid\n",
     "* Scheduled project review date/time: \n",
-    "* Instructor name: \n",
-    "* Blog post URL:\n"
+    "* Instructor name: Maryann Mwikali\n",
+    "* Blog post URL: \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BUSINESS UNDERSTANDING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BUSINESS OVERVIEW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A real estate agency aims to provide detailed pricing models to their clients showing how different features impact home sale prices.By understanding these impacts,the agency can advise sellers on how to enhance their property to maximize sale price and assist buyers in evaluating potential homes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CHALLENGES:\n",
+    "\n",
+    "1.The King County real estate market is subject to fluctuations influenced by economic conditions, interest rates, and other external factors, posing challenges in predicting property values accurately.\n",
+    "\n",
+    "2.The presence of numerous real estate agencies and agents vying for clients' attention intensifies competition, requiring innovative strategies to stand out and attract business.\n",
+    "\n",
+    "3.Ensuring the accuracy and completeness of data sources, as well as accessing relevant datasets for analysis, presents challenges in developing robust pricing models and predictive analytics.\n",
+    "\n",
+    "4.Ongoing regulatory changes, such as zoning regulations, tax policies, and housing laws, can impact market dynamics and require adaptation to ensure compliance and mitigate risks.\n",
+    "\n",
+    "#### PROPOSED SOLUTION:\n",
+    "\n",
+    "1.Implement sophisticated data analytics techniques, including machine learning algorithms, to analyze historical sales data, identify trends, and predict future property values with greater accuracy.\n",
+    "\n",
+    "2.Develop customized pricing models that consider a wide range of factors, including property features, neighborhood characteristics, market demand, and buyer preferences, to provide tailored pricing recommendations for each property.\n",
+    "\n",
+    "3.Establish a framework for continuous monitoring of market trends and model performance, allowing for timely adjustments to pricing strategies and recommendations based on changing market conditions.\n",
+    "\n",
+    "4.Foster collaborations with data providers, industry experts, and technology partners to access additional data sources, enhance analytical capabilities, and stay abreast of best practices in real estate valuation and predictive modeling.\n",
+    "\n",
+    "In conclusion, by leveraging advanced analytics and innovative strategies, the real estate agency can overcome challenges in the dynamic King County market and provide clients with valuable insights and recommendations to optimize their real estate transactions. Through continuous adaptation, collaboration, and a client-centric approach, the agency can achieve sustainable growth, enhance competitiveness, and deliver exceptional value to clients in the ever-evolving real estate landscape."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PROBLEM STATEMENT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "The real estate agency seeks to develop sophisticated pricing models that analyze the King County housing market data to determine how different features influence home sale prices. By understanding these impacts, the agency aims to:\n",
+    "1. Assist sellers in optimizing their property to maximize sale price: Sellers will benefit from tailored recommendations on which features to enhance or highlight to increase the value of their homes. By leveraging insights from the pricing models, sellers can make informed decisions about renovations, upgrades, or staging strategies to attract potential buyers and achieve optimal sale prices.\n",
+    "2. Empower buyers to make informed purchasing decisions: Buyers will gain valuable insights into how various property features correlate with sale prices. Armed with this knowledge, buyers can prioritize their preferences and make informed decisions when evaluating potential homes. Additionally, the agency can provide guidance on negotiating strategies based on the perceived value of different features.\n",
+    "3. Enhance the agency's competitive advantage: By offering advanced pricing models that provide granular insights into the factors influencing home sale prices, the agency can differentiate itself in the market. This will attract both sellers seeking to maximize their returns and buyers seeking expert guidance in their property search process.\n",
+    "\n",
+    "Overall, the development of detailed pricing models will enable the real estate agency to provide superior value to its clients, facilitate more informed decision-making processes, and maintain a competitive edge in the dynamic King County housing market."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DATA UNDERSTANDING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The King County House sale dataset contains information regarding houses sold during the one year period ranging from May 2014 to May 2015.\n",
+    "\n",
+    "In order to understand what each column in our data frame represents, a data dictionary is displayed below:\n",
+    "\n",
+    "##### TARGET/DEPENDENT VARIABLE:\n",
+    "\n",
+    "price — price of each home sold\n",
+    "\n",
+    "##### PREDICTORS/INDEPENDENT VARIABLES:\n",
+    "\n",
+    "id — unique identifier for a house\n",
+    "\n",
+    "date — date of the home sale\n",
+    "\n",
+    "bedrooms — number of bedrooms\n",
+    "\n",
+    "bathrooms — number of bathrooms\n",
+    "\n",
+    "sqft_living — square footage of the house’s interior living space\n",
+    "\n",
+    "sqft_lot — square footage of the land space\n",
+    "\n",
+    "floors — number of floors\n",
+    "\n",
+    "waterfront — does the house have a view to the waterfront?\n",
+    "\n",
+    "view — an index from 0 to 4 of how good the view of the property was\n",
+    "\n",
+    "condition — an index from 1 to 5 on the condition of the house\n",
+    "grade — an index from 1 to 13, where 1–3 falls short of building construction and design, 7 has an average level of construction and design, and 11–13 have a high quality level of construction and design\n",
+    "\n",
+    "sqft_above — square feet above ground\n",
+    "\n",
+    "sqft_basement — square feet below ground\n",
+    "\n",
+    "yr_built— the year the house was initially built\n",
+    "\n",
+    "yr_renovated — the year of the house’s last renovation (0 if never renovated)\n",
+    "\n",
+    "zipcode — zip\n",
+    "\n",
+    "lat — latitude coordinate\n",
+    "\n",
+    "long — longitude coordinate\n",
+    "\n",
+    "sqft_living15 — average size of interior housing living space for the closest 15 houses, in square feet\n",
+    "\n",
+    "sqft_lot15 — average size of land lot for the closest 15 houses, in square feet\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DATA PREPROCESSING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This dataset contains information on house sales in King County, WA between May 2014 and May 2015 and is found in this project's repository. The data cleaning on this dataset will be extremely helpful when creating a regression model for predicting house prices:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### IMPORTING NECESSARY LIBRARIES"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here - remember to use markdown cells for comments as well!"
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### LOADING THE DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "dataset = pd.read_csv(\"data/kc_house_data.csv\",index_col = 0 )\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "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>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2/25/2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                             \n",
+       "7129300520  10/13/2014  221900.0         3       1.00         1180      5650   \n",
+       "6414100192   12/9/2014  538000.0         3       2.25         2570      7242   \n",
+       "5631500400   2/25/2015  180000.0         2       1.00          770     10000   \n",
+       "2487200875   12/9/2014  604000.0         4       3.00         1960      5000   \n",
+       "1954400510   2/18/2015  510000.0         3       2.00         1680      8080   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  sqft_above  \\\n",
+       "id                                                                          \n",
+       "7129300520     1.0        NaN  NONE    Average      7 Average        1180   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average        2170   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average         770   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average        1050   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good        1680   \n",
+       "\n",
+       "           sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "id                                                                            \n",
+       "7129300520           0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "6414100192         400.0      1951        1991.0    98125  47.7210 -122.319   \n",
+       "5631500400           0.0      1933           NaN    98028  47.7379 -122.233   \n",
+       "2487200875         910.0      1965           0.0    98136  47.5208 -122.393   \n",
+       "1954400510           0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "\n",
+       "            sqft_living15  sqft_lot15  \n",
+       "id                                     \n",
+       "7129300520           1340        5650  \n",
+       "6414100192           1690        7639  \n",
+       "5631500400           2720        8062  \n",
+       "2487200875           1360        5000  \n",
+       "1954400510           1800        7503  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking the first five rows\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "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>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>2.159700e+04</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>17755.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "      <td>21597.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.402966e+05</td>\n",
+       "      <td>3.373200</td>\n",
+       "      <td>2.115826</td>\n",
+       "      <td>2080.321850</td>\n",
+       "      <td>1.509941e+04</td>\n",
+       "      <td>1.494096</td>\n",
+       "      <td>1788.596842</td>\n",
+       "      <td>1970.999676</td>\n",
+       "      <td>83.636778</td>\n",
+       "      <td>98077.951845</td>\n",
+       "      <td>47.560093</td>\n",
+       "      <td>-122.213982</td>\n",
+       "      <td>1986.620318</td>\n",
+       "      <td>12758.283512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.673681e+05</td>\n",
+       "      <td>0.926299</td>\n",
+       "      <td>0.768984</td>\n",
+       "      <td>918.106125</td>\n",
+       "      <td>4.141264e+04</td>\n",
+       "      <td>0.539683</td>\n",
+       "      <td>827.759761</td>\n",
+       "      <td>29.375234</td>\n",
+       "      <td>399.946414</td>\n",
+       "      <td>53.513072</td>\n",
+       "      <td>0.138552</td>\n",
+       "      <td>0.140724</td>\n",
+       "      <td>685.230472</td>\n",
+       "      <td>27274.441950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>7.800000e+04</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>5.200000e+02</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>1900.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98001.000000</td>\n",
+       "      <td>47.155900</td>\n",
+       "      <td>-122.519000</td>\n",
+       "      <td>399.000000</td>\n",
+       "      <td>651.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.220000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.750000</td>\n",
+       "      <td>1430.000000</td>\n",
+       "      <td>5.040000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1190.000000</td>\n",
+       "      <td>1951.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98033.000000</td>\n",
+       "      <td>47.471100</td>\n",
+       "      <td>-122.328000</td>\n",
+       "      <td>1490.000000</td>\n",
+       "      <td>5100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>1910.000000</td>\n",
+       "      <td>7.618000e+03</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1560.000000</td>\n",
+       "      <td>1975.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98065.000000</td>\n",
+       "      <td>47.571800</td>\n",
+       "      <td>-122.231000</td>\n",
+       "      <td>1840.000000</td>\n",
+       "      <td>7620.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.450000e+05</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2550.000000</td>\n",
+       "      <td>1.068500e+04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2210.000000</td>\n",
+       "      <td>1997.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98118.000000</td>\n",
+       "      <td>47.678000</td>\n",
+       "      <td>-122.125000</td>\n",
+       "      <td>2360.000000</td>\n",
+       "      <td>10083.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.700000e+06</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>13540.000000</td>\n",
+       "      <td>1.651359e+06</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>9410.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>98199.000000</td>\n",
+       "      <td>47.777600</td>\n",
+       "      <td>-121.315000</td>\n",
+       "      <td>6210.000000</td>\n",
+       "      <td>871200.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n",
+       "count  2.159700e+04  21597.000000  21597.000000  21597.000000  2.159700e+04   \n",
+       "mean   5.402966e+05      3.373200      2.115826   2080.321850  1.509941e+04   \n",
+       "std    3.673681e+05      0.926299      0.768984    918.106125  4.141264e+04   \n",
+       "min    7.800000e+04      1.000000      0.500000    370.000000  5.200000e+02   \n",
+       "25%    3.220000e+05      3.000000      1.750000   1430.000000  5.040000e+03   \n",
+       "50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n",
+       "75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068500e+04   \n",
+       "max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n",
+       "\n",
+       "             floors    sqft_above      yr_built  yr_renovated       zipcode  \\\n",
+       "count  21597.000000  21597.000000  21597.000000  17755.000000  21597.000000   \n",
+       "mean       1.494096   1788.596842   1970.999676     83.636778  98077.951845   \n",
+       "std        0.539683    827.759761     29.375234    399.946414     53.513072   \n",
+       "min        1.000000    370.000000   1900.000000      0.000000  98001.000000   \n",
+       "25%        1.000000   1190.000000   1951.000000      0.000000  98033.000000   \n",
+       "50%        1.500000   1560.000000   1975.000000      0.000000  98065.000000   \n",
+       "75%        2.000000   2210.000000   1997.000000      0.000000  98118.000000   \n",
+       "max        3.500000   9410.000000   2015.000000   2015.000000  98199.000000   \n",
+       "\n",
+       "                lat          long  sqft_living15     sqft_lot15  \n",
+       "count  21597.000000  21597.000000   21597.000000   21597.000000  \n",
+       "mean      47.560093   -122.213982    1986.620318   12758.283512  \n",
+       "std        0.138552      0.140724     685.230472   27274.441950  \n",
+       "min       47.155900   -122.519000     399.000000     651.000000  \n",
+       "25%       47.471100   -122.328000    1490.000000    5100.000000  \n",
+       "50%       47.571800   -122.231000    1840.000000    7620.000000  \n",
+       "75%       47.678000   -122.125000    2360.000000   10083.000000  \n",
+       "max       47.777600   -121.315000    6210.000000  871200.000000  "
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21597 entries, 7129300520 to 1523300157\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   date           21597 non-null  object \n",
+      " 1   price          21597 non-null  float64\n",
+      " 2   bedrooms       21597 non-null  int64  \n",
+      " 3   bathrooms      21597 non-null  float64\n",
+      " 4   sqft_living    21597 non-null  int64  \n",
+      " 5   sqft_lot       21597 non-null  int64  \n",
+      " 6   floors         21597 non-null  float64\n",
+      " 7   waterfront     19221 non-null  object \n",
+      " 8   view           21534 non-null  object \n",
+      " 9   condition      21597 non-null  object \n",
+      " 10  grade          21597 non-null  object \n",
+      " 11  sqft_above     21597 non-null  int64  \n",
+      " 12  sqft_basement  21597 non-null  object \n",
+      " 13  yr_built       21597 non-null  int64  \n",
+      " 14  yr_renovated   17755 non-null  float64\n",
+      " 15  zipcode        21597 non-null  int64  \n",
+      " 16  lat            21597 non-null  float64\n",
+      " 17  long           21597 non-null  float64\n",
+      " 18  sqft_living15  21597 non-null  int64  \n",
+      " 19  sqft_lot15     21597 non-null  int64  \n",
+      "dtypes: float64(6), int64(8), object(6)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The columns in the dataset are integers,floats and objects representing categorical variables.Some columns contain missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "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>Text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td># Column Names and Descriptions for King Count...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>* `id` - Unique identifier for a house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>* `date` - Date house was sold\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>* `price` - Sale price (prediction target)\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>* `bedrooms` - Number of bedrooms\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>* `bathrooms` - Number of bathrooms\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>* `sqft_living` - Square footage of living spa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>* `sqft_lot` - Square footage of the lot\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>* `floors` - Number of floors (levels) in house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>* `waterfront` - Whether the house is on a wat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>* Includes Duwamish, Elliott Bay, Puget Soun...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>* `view` - Quality of view from house\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>* Includes views of Mt. Rainier, Olympics, C...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>* `condition` - How good the overall condition...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>* See the [King County Assessor Website](htt...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>* `grade` - Overall grade of the house. Relate...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>* See the [King County Assessor Website](htt...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>* `sqft_above` - Square footage of house apart...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>* `sqft_basement` - Square footage of the base...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>* `yr_built` - Year when house was built\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>* `yr_renovated` - Year when house was renovat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>* `zipcode` - ZIP Code used by the United Stat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>* `lat` - Latitude coordinate\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>* `long` - Longitude coordinate\\n</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>* `sqft_living15` - The square footage of inte...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>* `sqft_lot15` - The square footage of the lan...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 Text\n",
+       "0   # Column Names and Descriptions for King Count...\n",
+       "1            * `id` - Unique identifier for a house\\n\n",
+       "2                    * `date` - Date house was sold\\n\n",
+       "3        * `price` - Sale price (prediction target)\\n\n",
+       "4                 * `bedrooms` - Number of bedrooms\\n\n",
+       "5               * `bathrooms` - Number of bathrooms\\n\n",
+       "6   * `sqft_living` - Square footage of living spa...\n",
+       "7          * `sqft_lot` - Square footage of the lot\\n\n",
+       "8   * `floors` - Number of floors (levels) in house\\n\n",
+       "9   * `waterfront` - Whether the house is on a wat...\n",
+       "10    * Includes Duwamish, Elliott Bay, Puget Soun...\n",
+       "11            * `view` - Quality of view from house\\n\n",
+       "12    * Includes views of Mt. Rainier, Olympics, C...\n",
+       "13  * `condition` - How good the overall condition...\n",
+       "14    * See the [King County Assessor Website](htt...\n",
+       "15  * `grade` - Overall grade of the house. Relate...\n",
+       "16    * See the [King County Assessor Website](htt...\n",
+       "17  * `sqft_above` - Square footage of house apart...\n",
+       "18  * `sqft_basement` - Square footage of the base...\n",
+       "19         * `yr_built` - Year when house was built\\n\n",
+       "20  * `yr_renovated` - Year when house was renovat...\n",
+       "21  * `zipcode` - ZIP Code used by the United Stat...\n",
+       "22                    * `lat` - Latitude coordinate\\n\n",
+       "23                  * `long` - Longitude coordinate\\n\n",
+       "24  * `sqft_living15` - The square footage of inte...\n",
+       "25  * `sqft_lot15` - The square footage of the lan..."
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Reading the column_names.md Dataset\n",
+    "\n",
+    "with open('data/column_names.md', 'r') as file:\n",
+    "    md_lines = file.readlines()\n",
+    "\n",
+    "dataset_col = pd.DataFrame({'Text': md_lines})\n",
+    "\n",
+    "dataset_col"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(21597, 20)"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data has 21597 entries and 20 columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
+       "       'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
+       "       'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
+       "       'sqft_living15', 'sqft_lot15'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking the columns in the dataset\n",
+    "dataset.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "int64      8\n",
+      "object     6\n",
+      "float64    6\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#CHECKING THE DATATYPES OF THE FEATURES ABOVE\n",
+    "column_data_types_counts = dataset.dtypes.value_counts()\n",
+    "\n",
+    "print(column_data_types_counts)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### DATA CLEANING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data Cleaning is the First Step Towards Predictive Accuracy,In order to produce a trustworthy and accurate model,we will perform data cleaning beforehand!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### CHECKING FOR MISSING VALUES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Missing values are often times problematic and should always be checked when reviewing a dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date             False\n",
+       "price            False\n",
+       "bedrooms         False\n",
+       "bathrooms        False\n",
+       "sqft_living      False\n",
+       "sqft_lot         False\n",
+       "floors           False\n",
+       "waterfront        True\n",
+       "view              True\n",
+       "condition        False\n",
+       "grade            False\n",
+       "sqft_above       False\n",
+       "sqft_basement    False\n",
+       "yr_built         False\n",
+       "yr_renovated      True\n",
+       "zipcode          False\n",
+       "lat              False\n",
+       "long             False\n",
+       "sqft_living15    False\n",
+       "sqft_lot15       False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking if there are any missing values in the features\n",
+    "dataset.isnull().any()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date              0.000000\n",
+       "price             0.000000\n",
+       "bedrooms          0.000000\n",
+       "bathrooms         0.000000\n",
+       "sqft_living       0.000000\n",
+       "sqft_lot          0.000000\n",
+       "floors            0.000000\n",
+       "waterfront       11.001528\n",
+       "view              0.291707\n",
+       "condition         0.000000\n",
+       "grade             0.000000\n",
+       "sqft_above        0.000000\n",
+       "sqft_basement     0.000000\n",
+       "yr_built          0.000000\n",
+       "yr_renovated     17.789508\n",
+       "zipcode           0.000000\n",
+       "lat               0.000000\n",
+       "long              0.000000\n",
+       "sqft_living15     0.000000\n",
+       "sqft_lot15        0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking for the percentage of missing values per column\n",
+    "dataset.isnull().sum() / len(dataset) * 100\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Waterfront, view, and yr_renovated have missing values. If we decide to use these variables in the predictive model it will be problematic hence we'll fix the missing values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### FIXING MISSING VALUES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned above, waterfront, view, and yr_renovated are the only columns with missing values.We'll fix the missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "waterfront\n",
+      "NO     0.992404\n",
+      "YES    0.007596\n",
+      "Name: waterfront, dtype: float64\n",
+      "------------------\n",
+      "view\n",
+      "NONE         0.901923\n",
+      "AVERAGE      0.044441\n",
+      "GOOD         0.023591\n",
+      "FAIR         0.015325\n",
+      "EXCELLENT    0.014721\n",
+      "Name: view, dtype: float64\n",
+      "------------------\n",
+      "yr_renovated\n",
+      "0.0       0.958096\n",
+      "2014.0    0.004112\n",
+      "2003.0    0.001746\n",
+      "2013.0    0.001746\n",
+      "2007.0    0.001690\n",
+      "Name: yr_renovated, dtype: float64\n",
+      "------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "#checking the percentage\n",
+    "for col in [\"waterfront\", \"view\", \"yr_renovated\"]:\n",
+    "    print(col)\n",
+    "    print(dataset[col].value_counts(normalize = True).sort_values(ascending = False).head())\n",
+    "    print(\"------------------\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results shown above clearly show that 0 is the most common value for all three columns!,where NONE in the views column is equivalent to 0.In order to retain as much data as possible, we’ll fill the missing values of each column by randomly choosing a unique value given the appropriate weights. For example, a missing value in the waterfront column will be replaced by 0 with a 99% chance and 1 with a 1% chance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The number of missing values in waterfront is: 0\n",
+      "The number of missing values in view is: 0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The number of missing values in yr_renovated is: 0\n",
+      "--------------------------------------\n",
+      "Missing values per column:\n",
+      "date             0\n",
+      "price            0\n",
+      "bedrooms         0\n",
+      "bathrooms        0\n",
+      "sqft_living      0\n",
+      "sqft_lot         0\n",
+      "floors           0\n",
+      "waterfront       0\n",
+      "view             0\n",
+      "condition        0\n",
+      "grade            0\n",
+      "sqft_above       0\n",
+      "sqft_basement    0\n",
+      "yr_built         0\n",
+      "yr_renovated     0\n",
+      "zipcode          0\n",
+      "lat              0\n",
+      "long             0\n",
+      "sqft_living15    0\n",
+      "sqft_lot15       0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "def replace_missing(val, probs):\n",
+    "    if pd.isnull(val):\n",
+    "        return np.random.choice(probs.index, p=probs)\n",
+    "    else:\n",
+    "        return val\n",
+    "\n",
+    "for col in [\"waterfront\", \"view\", \"yr_renovated\"]:\n",
+    "    # Check if there are missing values in the column\n",
+    "    if dataset[col].isnull().sum() > 0:\n",
+    "        # Calculate the probabilities of each unique value\n",
+    "        unique_p = dataset[col].value_counts(normalize=True)\n",
+    "        # Apply the function to replace missing values\n",
+    "        dataset[col] = dataset[col].apply(replace_missing, args=(unique_p,))\n",
+    "        print(\"The number of missing values in {} is:\".format(col), dataset[col].isnull().sum())\n",
+    "\n",
+    "print(\"--------------------------------------\")\n",
+    "print(\"Missing values per column:\")\n",
+    "# Last check to see if there are missing values\n",
+    "print(dataset.isnull().sum())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No more missing values and we retained all data we started off with."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CHECKING FOR DUPLICATED VALUES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Duplicates are also problematic and are far more subtle than missing values. Given that a single house could’ve been sold at different points in time, we’ll define a duplicate as any row that has the same `id` and `date` as any other row in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if the number of duplicates is 0\n",
+    "dataset.duplicated().sum() == 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are no duplicated values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### DROPPING THE COLUMNS THAT WE WON'T BE USING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = dataset.drop(columns=['sqft_basement', 'zipcode', 'lat', 'long', 'sqft_living15'\n",
+    "                     ,'sqft_lot15','sqft_above'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "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>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7129300520</th>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6414100192</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5631500400</th>\n",
+       "      <td>2/25/2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2487200875</th>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954400510</th>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+       "id                                                                             \n",
+       "7129300520  10/13/2014  221900.0         3       1.00         1180      5650   \n",
+       "6414100192   12/9/2014  538000.0         3       2.25         2570      7242   \n",
+       "5631500400   2/25/2015  180000.0         2       1.00          770     10000   \n",
+       "2487200875   12/9/2014  604000.0         4       3.00         1960      5000   \n",
+       "1954400510   2/18/2015  510000.0         3       2.00         1680      8080   \n",
+       "\n",
+       "            floors waterfront  view  condition          grade  yr_built  \\\n",
+       "id                                                                        \n",
+       "7129300520     1.0         NO  NONE    Average      7 Average      1955   \n",
+       "6414100192     2.0         NO  NONE    Average      7 Average      1951   \n",
+       "5631500400     1.0         NO  NONE    Average  6 Low Average      1933   \n",
+       "2487200875     1.0         NO  NONE  Very Good      7 Average      1965   \n",
+       "1954400510     1.0         NO  NONE    Average         8 Good      1987   \n",
+       "\n",
+       "            yr_renovated  \n",
+       "id                        \n",
+       "7129300520           0.0  \n",
+       "6414100192        1991.0  \n",
+       "5631500400           0.0  \n",
+       "2487200875           0.0  \n",
+       "1954400510           0.0  "
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#the features that we will be working with\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Visual Exploration of Key Features (EDA)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Price Distribution\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.histplot(dataset['price'], kde=True, color='blue')\n",
+    "plt.title('Distribution of House Prices')\n",
+    "plt.xlabel('Price')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This histogram represents the frequency of houses at different price points. The plot also includes a Kernel Density Estimate (KDE), which is the smooth curve showing the distribution shape.\n",
+    "\n",
+    "Central Tendency: The peak of the KDE suggests where the bulk of house prices are concentrated. A peak in a lower price range might suggest affordability is more common.\n",
+    "\n",
+    "Spread: The width of the distribution indicates variability in house prices. A wider spread means prices are highly variable, impacting buyer's options.\n",
+    "\n",
+    "Skewness: If the tail of the histogram extends further to the right, it indicates a right-skewed distribution, meaning there are a few very expensive houses.\n",
+    "\n",
+    "Outliers: Extreme values on the higher end can be seen as separate, sparse bars, indicating potential outliers.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Relation between Price and Bedrooms/Bathrooms\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.boxplot(x='bedrooms', y='price', data=dataset)\n",
+    "plt.title('Price Distribution by Number of Bedrooms')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(x='bathrooms', y='price', data=dataset)\n",
+    "plt.title('Price Distribution by Number of Bathrooms')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These boxplots show how house prices vary with the number of bedrooms and bathrooms.\n",
+    "\n",
+    "Median Price: The line in the middle of each box indicates the median price for each category, providing a sense of the typical price.\n",
+    "\n",
+    "Price Range: The height of the boxes shows the interquartile range (middle 50% of data), which helps understand the typical price variation within each category.\n",
+    "\n",
+    "Outliers: Points above or below the whiskers represent outliers, which are significantly higher or lower priced houses than typical for that number of bedrooms or bathrooms.\n",
+    "\n",
+    "Trends: Generally, more bedrooms and bathrooms correlate with higher prices, but diminishing returns or price saturation can be observed in higher categories."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Year Built and Renovated Impact\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.scatterplot(x='yr_built', y='price', data=dataset, alpha=0.5)\n",
+    "plt.title('Price vs. Year Built')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.scatterplot(x='yr_renovated', y='price', data=dataset, alpha=0.5)\n",
+    "plt.title('Price vs. Year Renovated')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scatter plots showing how prices compare with the year the house was built and the year it was renovated.\n",
+    "\n",
+    "Trends Over Time: Older houses might not necessarily be cheaper; historical or well-maintained homes can also command high prices.\n",
+    "\n",
+    "Renovation Impact: Renovated homes can vary widely in price, suggesting that the quality and extent of renovations, along with the original house's characteristics, significantly impact value.\n",
+    "\n",
+    "Age and Price: Newer homes tend to have a more consistent price range, often at a premium due to modern features and less anticipated maintenance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Statistical Summaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Boxplots for Outlier Detection\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.boxplot(data=dataset[['price', 'sqft_living', 'sqft_lot']], palette='Set2')\n",
+    "plt.title('Boxplot of Price, Sqft Living, and Sqft Lot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Boxplots for price, square footage of the living area, and lot size to detect outliers.\n",
+    "\n",
+    "Price and Size Relationship: Understanding how the size of the house and the lot correlates with price and identifying extreme values in each category.\n",
+    "\n",
+    "Outlier Detection: Significant outliers can skew analysis and model predictions, so they might need to be handled (e.g., by capping, transformation, or removal).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Correlation Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Heatmap of Feature Correlation\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "sns.heatmap(dataset.corr(), annot=True, fmt=\".2f\", cmap='coolwarm')\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A heatmap showing the correlation coefficients between different features in the dataset.\n",
+    "\n",
+    "Strong Correlations: High positive values (closer to +1.0) indicate a strong positive relationship between features, useful for predicting one from another.\n",
+    "\n",
+    "Negative Correlations: Values closer to -1.0 show a strong inverse relationship.\n",
+    "\n",
+    "Multicollinearity Concerns: Features that are highly correlated with others (except price) may lead to multicollinearity in regression models, potentially necessitating removal or combining of features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -40,7 +1721,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,