final_report_plus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"6\"> Zillow Property Value Predictions</font> <br><br>\n",
    "<font size=\"5\"> Description </font><br><br>\n",
    "In the rapidly evolving real estate market, accurate property valuation holds immense importance for homeowners and property investors alike. This project is dedicated to developing a predictive model for property values of Single Family Properties that underwent transactions during the year 2017. It directly addresses the critical need to provide accurate property values.<br><br>\n",
    "<font size=\"5\"> Goals </font><br><br>\n",
    "The primary goal of the Zillow Property Value Predictions project is to predict property values accurately. To achieve this goal, we have established the following objectives:\n",
    "\n",
    "- **Data Collection and Preprocessing:** Gather and clean Zillow property data to create a comprehensive dataset suitable for analysis.\n",
    "\n",
    "- **Exploratory Data Analysis:** Perform exploratory analysis to identify trends, patterns, and potential correlations related to the value of the properties.\n",
    "\n",
    "- **Feature Importance Determination:** Employ machine learning techniques to assess the importance of various features in predicting property values, aiding in identifying critical factors.\n",
    "\n",
    "- **Model Building and Evaluation:** Develop predictive models for property values, compare their performance, and select the most effective one for accurate value prediction.\n",
    "\n",
    "\n",
    "<font size=\"5\">Dictionary</font><br>\n",
    "\n",
    "| Term Name              | Description                                                |\n",
    "|------------------------|------------------------------------------------------------|\n",
    "| bedrooms               | Number of bedrooms in the property.                       |\n",
    "| bathrooms              | Number of bathrooms in the property.                      |\n",
    "| area                   | Area of the property in square feet.                      |\n",
    "| value                  | Property value in dollars.                                |\n",
    "| year                   | Year the property was built.                              |\n",
    "| county                 | County where the property is located.                     |\n",
    "| state                  | State where the property is located.                      |\n",
    "| total_rooms            | Total number of rooms (bedrooms + bathrooms).             |\n",
    "| age                    | Age of the property (current year - year built).          |\n",
    "| bdrm_area_ratio        | Bedroom-to-area ratio.                                    |\n",
    "| bath_bdrm_ratio        | Bathroom-to-bedroom ratio.                               |\n",
    "| group_area             | Categorical group based on property area.                 |\n",
    "| group_age              | Categorical group based on property age.                  |\n",
    "| FIPS Code              | A numerical code used for mapping locations to county and state names in the dataset. |\n",
    "| RMSE                   | Root Mean Squared Error, a measure of prediction accuracy in regression models. |\n",
    "| Outliers               | Data points that deviate significantly from the majority of the data and can affect analysis and modeling. |\n",
    "| Feature Selection      | Techniques used to select relevant features: SelectKBest, RFE (Recursive Feature Elimination), and Lasso.\n",
    "| Polynomial Features    | New features created by raising existing features to different powers to capture non-linear relationships. |\n",
    "\n",
    "<br><br><br><br>\n",
    "<font size=\"5\"> Imports </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports:\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# import os\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, LassoLars\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "import prepare as prep\n",
    "import explore as exp\n",
    "import visuals as vis\n",
    "import model as mdl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Acquire</font>\n",
    "<br>\n",
    "<br>\n",
    "- Data acquired from the 'Zillow' SQL database.\n",
    "- The database was queried for specific property data for the year `2017`.\n",
    "- The data consists of `52,246` rows and `7 columns`.\n",
    "- Each row represents a property with its respective attributes.\n",
    "- Each column represents a specific property feature\n",
    "\n",
    "<font size=\"5\">Prepare</font>\n",
    "<br>\n",
    "<br>\n",
    "**Column Renaming:**\n",
    "- Columns were renamed for clarity and consistency:\n",
    "  - bedroomcnt → bedrooms\n",
    "  - bathroomcnt → bathrooms\n",
    "  - calculatedfinishedsquarefeet → area\n",
    "  - taxvaluedollarcnt → value\n",
    "  - yearbuilt → year\n",
    "\n",
    "**FIPS Code Mapping:**\n",
    "- FIPS codes were mapped to county and state name. Two new columns, `county` and `state`, were added to the DataFrame based on the 'fips' column.\n",
    "\n",
    "**Data Cleaning:**\n",
    "- Rows with missing values (nulls) were removed from the dataset.\n",
    "  - Found 122 observations with missing area or year built. \n",
    "- Rows where either the number of bedrooms or bathrooms was equal to 0 were excluded.\n",
    "  - Found 73 observations with 0 bedrooms or bathrooms\n",
    "- Columns 'parcelid' and 'fips' were dropped as they were unnecessary.\n",
    "\n",
    "**Data Type Conversion:**\n",
    "- The data types of specific columns were converted to the 'int' data type for consistency.\n",
    "  - The columns converted included 'bedrooms,' 'area,' 'value,' and 'year.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">A brief look at the data:</font><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>value</th>\n",
       "      <th>year</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3100</td>\n",
       "      <td>1023282</td>\n",
       "      <td>1998</td>\n",
       "      <td>Orange</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1465</td>\n",
       "      <td>464000</td>\n",
       "      <td>1967</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1243</td>\n",
       "      <td>564778</td>\n",
       "      <td>1962</td>\n",
       "      <td>Orange</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2376</td>\n",
       "      <td>145143</td>\n",
       "      <td>1970</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2962</td>\n",
       "      <td>773303</td>\n",
       "      <td>1950</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  area    value  year       county state\n",
       "0         4        3.5  3100  1023282  1998       Orange    CA\n",
       "1         2        1.0  1465   464000  1967      Ventura    CA\n",
       "2         3        2.0  1243   564778  1962       Orange    CA\n",
       "3         4        3.0  2376   145143  1970  Los Angeles    CA\n",
       "4         4        3.0  2962   773303  1950  Los Angeles    CA"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data\n",
    "df = prep.prepare_pipeline()\n",
    "# Display the first few rows of the dataset.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">Lets take a look at the distribution of the target.</font><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display overall value distribution\n",
    "vis.plot_value_distribution(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">**Note:** X axis was limited to $3M</font><br>\n",
    "\n",
    "- I hid less than 1% of the top property values in order to properly visualize the distribution.\n",
    "- Max property value found in the dataset is $49M\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">Lets take a look at the distribution of the other features</font><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the distribution of Bedrooms, Bathrooms, Area, Year and value counts for county. \n",
    "vis.plot_features_distribution(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">**Note:** Once again, X axis was limited.</font><br>\n",
    "\n",
    "- Bedrooms and bathrooms limited to 7 which accounts for less than 0.25% of the total observations\n",
    "    - Max bedrooms: 14\n",
    "    - Max bathrooms:18<br><br>\n",
    "- Area limited to 6,000 sqft which accounts for about 0.5% of the total observations. \n",
    "    - Max area: 20,000+ sqft\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Exploration</font><br>\n",
    "\n",
    "\n",
    "\n",
    "**Data Splitting:**\n",
    "- The data was split into `training`, `validation`, and `test` sets for exploration / machine learning purposes. A `70-15-15` split was applied, with 70% of the data used for training and the remaining 30% split evenly between validation and test sets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the zillow_pipeline function to obtain a DataFrame containing Zillow data.\n",
    "train, val, test = prep.explore_pipeline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\"> **Hypothesis 1: Property Size Impact**</font>\n",
    "   <font size=\"3\"> \n",
    "   \n",
    "   - *Question:* `Does the size of a property impact its value?`\n",
    "   </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   - *Null Hypothesis (H0):* There is no significant relationship between property size (square footage) and property value.\n",
    "   - *Alternative Hypothesis (H1):* Property size (square footage) is significantly related to property value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize area and \"value\" using a scatter plot.\n",
    "vis.area_vs_value_trend_plt(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">**Note:** X and Y axis was limited.</font><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result: There is a significant monotonic relationship between area and value (p-value=0.0000, r-value=0.5333).\n"
     ]
    }
   ],
   "source": [
    "# Perform a Spearman rank-order correlation test to analyze the association between area and value.\n",
    "exp.perform_spearmanr_test(train,'area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis 1 Result:** `We reject the null hypothesis`, there is a statistically significant `moderate positive` monotonic relationship between the property values and the area(sqft). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\"> **Hypothesis 2: Property Age Impact**</font>\n",
    "   <font size=\"3\"> \n",
    "   \n",
    "   - *Question:* `Does the age of a property (year built) influence its value?`\n",
    "   </font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   - *Null Hypothesis (H0):* There is no significant relationship between the age of a property (year built) and property value.\n",
    "   - *Alternative Hypothesis (H1):* The age of a property (year built) is significantly related to property value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize year built and average value using a bar plot.\n",
    "vis.age_vs_value_plt(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result: There is a significant monotonic relationship between year and value (p-value=0.0000, r-value=0.2801).\n"
     ]
    }
   ],
   "source": [
    "# Perform a Spearman rank-order correlation test to analyze the association between year built and value.\n",
    "exp.perform_spearmanr_test(train,'year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis 2 Result:** `We reject the null hypothesis`, there is a statistically significant `weak positive` monotonic relationship between the property values and the area(sqft). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\"> **Hypothesis 3 & 4: Bedroom or Bathroom impact** </font>\n",
    "   <font size=\"3\"> \n",
    "   \n",
    "   - *Question:* `Do properties with a higher number of bedrooms or bathrooms tend to have higher values?`\n",
    "   </font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   - *Null Hypothesis (H0):* There is no significant difference in the distribution of value across different bathrooms or bedrooms.\n",
    "   - *Alternative Hypothesis (H1):* There is a significant difference in the distribution of value across different bathrooms or bedrooms.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize bedrooms and average value using a bar plot.\n",
    "vis.bedr_vs_value_plt(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize bathrooms and average value using a bar plot.\n",
    "vis.bathr_vs_value_plt(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal-Wallis Test\n",
      "-----------------------------------------------------\n",
      "Result: Reject the null hypothesis, There is a significant difference in the distribution of value across different bedrooms.\n",
      "H-statistic: 3543.5758\n",
      "P-value: 0.0\n",
      "Kruskal-Wallis Test\n",
      "-----------------------------------------------------\n",
      "Result: Reject the null hypothesis, There is a significant difference in the distribution of value across different bathrooms.\n",
      "H-statistic: 10145.1661\n",
      "P-value: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Perform a Kruskal-Wallis test to analyze the relationship between the 'bedrooms' variable and 'value'.\n",
    "exp.perform_kruskal(train, 'bedrooms', 'value')\n",
    "\n",
    "# Perform a Kruskal-Wallis test to analyze the relationship between the 'bathrooms' variable and 'value'.\n",
    "exp.perform_kruskal(train, 'bathrooms', 'value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis 3 Result:** `We reject the null hypothesis`, there is a significant difference in the distribution of value across different bedrooms.\n",
    "\n",
    "**Hypothesis 4 Result:** `We reject the null hypothesis`, there is a significant difference in the distribution of value across different bathrooms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\"> **Hypothesis 5: Location Impact**</font>\n",
    "   <font size=\"3\"> \n",
    "   \n",
    "   - *Question:* `Does the county of a property impact its value?`\n",
    "   </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize county and average value using a bar plot.\n",
    "vis.county_vs_value_plt(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   - *Null Hypothesis (H0):* There is no significant difference in the mean value across different counties.\n",
    "   - *Alternative Hypothesis (H1):* There is a significant difference in the mean value across at least two counties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal-Wallis Test\n",
      "-----------------------------------------------------\n",
      "Result: Reject the null hypothesis, There is a significant difference in the distribution of value across different county.\n",
      "H-statistic: 1281.9993\n",
      "P-value: 4.1436991204032774e-279\n"
     ]
    }
   ],
   "source": [
    "# Perform a Kruskal-Wallis test to analyze the relationship between the 'bathrooms' variable and 'value'.\n",
    "exp.perform_kruskal(train, 'county', 'value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis 5 Result:** `We reject the null hypothesis`, There is a significant difference in the distribution of value across different county."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Exploration Summary</font><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We performed `SpearmanR` test on: \n",
    "\n",
    " - Area\n",
    " - Year\n",
    "\n",
    "We performed `Kruskal-Wallis` test on:\n",
    " - Bedrooms\n",
    " - Bathrooms  \n",
    " - County\n",
    "\n",
    "`All indicated a significant relationship`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Feature Engineering</font><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6 additional columns:\n",
    "- *`'total_rooms'`*: Total number of rooms, calculated as the sum of bedrooms and bathrooms.\n",
    "- *`'age'`*:Age of each entry, calculated as the difference between the current year and the 'year' column.\n",
    "- *`'bdrm_area_ratio'`*: Bedroom-to-area ratio, calculated as 'area' divided by 'bedrooms'. (2,000sqft / 4bdrm = 500)\n",
    "- *`'bath_bdrm_ratio'`*: Bathroom-to-bedroom ratio, calculated as 'bathrooms' divided by 'bedrooms'. (2bthrm / 4bdrm = 0.5)\n",
    "- *`'group_area'`*: categorizes values in the 'area' column based on percentiles. (top_1%', 'middle_50%', 'bottom_1%', ...)\n",
    "- *`'group_age'`*: categorizes values in the 'age' column based on percentiles. (top_1%', 'middle_50%', 'bottom_1%', ...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add new features to the train dataset.\n",
    "train = mdl.features(train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>value</th>\n",
       "      <th>year</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>age</th>\n",
       "      <th>bdrm_area_ratio</th>\n",
       "      <th>bath_bdrm_ratio</th>\n",
       "      <th>group_area</th>\n",
       "      <th>group_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20022</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1686</td>\n",
       "      <td>169189</td>\n",
       "      <td>1947</td>\n",
       "      <td>Orange</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>76</td>\n",
       "      <td>562.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>middle_50%</td>\n",
       "      <td>top_20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22947</th>\n",
       "      <td>5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3658</td>\n",
       "      <td>1193666</td>\n",
       "      <td>1970</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>CA</td>\n",
       "      <td>8.5</td>\n",
       "      <td>53</td>\n",
       "      <td>731.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>top_10%</td>\n",
       "      <td>middle_50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18000</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1040</td>\n",
       "      <td>35983</td>\n",
       "      <td>1927</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>96</td>\n",
       "      <td>346.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>bottom_20%</td>\n",
       "      <td>top_10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10496</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2197</td>\n",
       "      <td>657564</td>\n",
       "      <td>1985</td>\n",
       "      <td>Orange</td>\n",
       "      <td>CA</td>\n",
       "      <td>5.5</td>\n",
       "      <td>38</td>\n",
       "      <td>732.333333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>middle_50%</td>\n",
       "      <td>bottom_25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41940</th>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2464</td>\n",
       "      <td>1096680</td>\n",
       "      <td>1987</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>36</td>\n",
       "      <td>616.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>top_25%</td>\n",
       "      <td>bottom_20%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bedrooms  bathrooms  area    value  year       county state  \\\n",
       "20022         3        2.0  1686   169189  1947       Orange    CA   \n",
       "22947         5        3.5  3658  1193666  1970      Ventura    CA   \n",
       "18000         3        1.0  1040    35983  1927  Los Angeles    CA   \n",
       "10496         3        2.5  2197   657564  1985       Orange    CA   \n",
       "41940         4        4.0  2464  1096680  1987  Los Angeles    CA   \n",
       "\n",
       "       total_rooms  age  bdrm_area_ratio  bath_bdrm_ratio  group_area  \\\n",
       "20022          5.0   76       562.000000         0.666667  middle_50%   \n",
       "22947          8.5   53       731.600000         0.700000     top_10%   \n",
       "18000          4.0   96       346.666667         0.333333  bottom_20%   \n",
       "10496          5.5   38       732.333333         0.833333  middle_50%   \n",
       "41940          8.0   36       616.000000         1.000000     top_25%   \n",
       "\n",
       "        group_age  \n",
       "20022     top_20%  \n",
       "22947  middle_50%  \n",
       "18000     top_10%  \n",
       "10496  bottom_25%  \n",
       "41940  bottom_20%  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first few rows of the train dataset. \n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the Min Max Scaler to scale the features (except the target feature)\n",
    "train = exp.MinMax_Scaler(train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform one-hot encoding to convert categorical features into numerical format.\n",
    "train = exp.hot_encode(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We performed feature selection using:\n",
    " \n",
    "- `SelectKbest`\n",
    "- `RFE`\n",
    "- `LASSO`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Kbest</th>\n",
       "      <th>RFE</th>\n",
       "      <th>Lasso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>area</td>\n",
       "      <td>bedrooms</td>\n",
       "      <td>area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>bathrooms</td>\n",
       "      <td>bedrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bdrm_area_ratio</td>\n",
       "      <td>total_rooms</td>\n",
       "      <td>bdrm_area_ratio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_rooms</td>\n",
       "      <td>age</td>\n",
       "      <td>bathrooms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Kbest          RFE            Lasso\n",
       "0             area     bedrooms             area\n",
       "1        bathrooms    bathrooms         bedrooms\n",
       "2  bdrm_area_ratio  total_rooms  bdrm_area_ratio\n",
       "3      total_rooms          age        bathrooms"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform feature selection analysis to identify the top 4 features that influence the value variable.\n",
    "exp.feature_selections_results(train, 'value', 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Feature Engineering Summary</font><br><br>\n",
    "\n",
    "After conducting the feature selection methods, `7 features` were selected for modeling. \n",
    "\n",
    "We are moving forward with the following features to modeling:\n",
    "\n",
    "Original\n",
    "- Area\n",
    "- Bedrooms\n",
    "- Bathrooms\n",
    "- Age\n",
    "\n",
    "Engineered\n",
    "- `total_rooms`\n",
    "- `bdrm_area_ratio`\n",
    "- `bath_bdrm_ratio`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Modeling</font><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the Root Mean Squared Error `(RMSE)` between actual target values and baseline predictions. <br>\n",
    "The baseline prediction is computed as the `mean` of the actual target values (y_train)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "755431.5736908859"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.eval_baseline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I will use the Root Mean Squared Error `(RMSE)` as my evaluation metric for regression models.\n",
    "- Models will be `evaluated on train and validate` data\n",
    "- Baseline RMSE is `$755,431.57` on train data\n",
    "- The model that performs the best will then be evaluated on test data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">Preprocessing for machine learning by: </font><br><br>\n",
    "\n",
    "- Drop: \n",
    "    - 'year'\n",
    "    - 'county'\n",
    "    - 'state'\n",
    "    - 'bath_bdrm_ratio'\n",
    "    - 'group_area'\n",
    "    - 'group_age'\n",
    "<br>\n",
    "<br>\n",
    "- Split:\n",
    "    - 70% training set\n",
    "    - 5% validation set\n",
    "    - 15% test set.\n",
    "<br>\n",
    "<br>\n",
    "- Filter train:\n",
    "    - Bedrooms: Drop more than 6 bedrooms (`bedrooms <= 6`)\n",
    "    - Bathrooms: Drop more than 6 bathrooms (`bathrooms <= 6`)\n",
    "    - Area: Drop less than 700sqft or more than 5,500sqft (`700 <= area <= 5,500`)\n",
    "    - bdrm_area_ratio: Drop less than 100sqft or more than 2,000sqft per bedroom. (`100 <= ratio <= 2,000`)\n",
    "    - Total dropped: `3%`\n",
    "<br>\n",
    "<br>\n",
    "- Separates features (X) and labels (y) for the training and validation sets\n",
    "- Scaling using Min-Max.\n",
    "<br><br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_val, y_val, X_test, y_test = mdl.mdata_pipeline(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last look at the data for modeling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>area</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>age</th>\n",
       "      <th>bdrm_area_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3491</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.245990</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.244679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15404</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.317850</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.308532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14924</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.130181</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.405797</td>\n",
       "      <td>0.080511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16572</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070194</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.485507</td>\n",
       "      <td>0.088469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46841</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.106020</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.775362</td>\n",
       "      <td>0.120304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bedrooms  bathrooms      area  total_rooms       age  bdrm_area_ratio\n",
       "3491        0.4        0.2  0.245990          0.3  0.391304         0.244679\n",
       "15404       0.4        0.4  0.317850          0.4  0.391304         0.308532\n",
       "14924       0.6        0.2  0.130181          0.4  0.405797         0.080511\n",
       "16572       0.4        0.0  0.070194          0.2  0.485507         0.088469\n",
       "46841       0.4        0.0  0.106020          0.2  0.775362         0.120304"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Models</font><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">XGBoost</font><br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train RMSE is $323,977.06.\n",
      "The validate RMSE is $495,722.13.\n"
     ]
    }
   ],
   "source": [
    "xgbr, model_results = mdl.train_model(XGBRegressor, X_train, y_train, X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">`Add Polynomial Features`</font><br><br>\n",
    "\n",
    " transforming the original features of a dataset into a set of polynomial features.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, X_test = mdl.poly_features(X_train, X_val, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">LinearRegression</font><br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train RMSE is $433,219.80.\n",
      "The validate RMSE is $484,395.01.\n"
     ]
    }
   ],
   "source": [
    "lrm, model_results = mdl.train_model(LinearRegression, X_train, y_train, X_val, y_val, model_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">LassoLars</font><br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train RMSE is $433,224.11.\n",
      "The validate RMSE is $484,373.41.\n"
     ]
    }
   ],
   "source": [
    "lasso, model_results = mdl.train_model(LassoLars, X_train, y_train, X_val, y_val, model_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">Random Forest</font><br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train RMSE is $182,225.41.\n",
      "The validate RMSE is $507,055.22.\n"
     ]
    }
   ],
   "source": [
    "rforest_test, model_results = mdl.train_model(RandomForestRegressor, X_train, y_train, X_val, y_val, model_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">`RMSE Results`</font><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train_RMSE</th>\n",
       "      <th>Val_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>$323,977.06</td>\n",
       "      <td>$495,722.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>$433,219.80</td>\n",
       "      <td>$484,395.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LassoLars</td>\n",
       "      <td>$433,224.11</td>\n",
       "      <td>$484,373.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>$182,225.41</td>\n",
       "      <td>$507,055.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model   Train_RMSE     Val_RMSE\n",
       "0           XGBRegressor  $323,977.06  $495,722.13\n",
       "1       LinearRegression  $433,219.80  $484,395.01\n",
       "2              LassoLars  $433,224.11  $484,373.41\n",
       "3  RandomForestRegressor  $182,225.41  $507,055.22"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Comparing Models</font><br><br>\n",
    "\n",
    " - RandomForestRegressor, XGBoost, LinearRegression and LassoLars models all performed better than baseline.\n",
    " - I have chosen to move forward with the `LassoLars` model as it performs slightly better than LinearRegression and had a the best overall predictive performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">LassoLars on Test Data</font><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test RMSE is $412,174.56.\n"
     ]
    }
   ],
   "source": [
    "test_results = mdl.test_model(lasso, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\">`RMSE Results on TEST`</font>\n",
    "#### $412K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Modeling Summary</font><br><br>\n",
    "\n",
    " - `LassoLars model scored RMSE $412k` compared to the `baseline of $755k`, it had an improvement of approx $343k."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Conclusions</font><br><br>\n",
    "\n",
    " - Found a strong relationship between area, bathrooms and bedrooms to the value of the property.\n",
    " - Engineered 6 new columns based on the relationships found.\n",
    "   - *'total_rooms'*\n",
    "   - *'age'*\n",
    "   - *'bdrm_area_ratio'*\n",
    "   - *'bath_bdrm_ratio'*\n",
    "   - *'group_area'*\n",
    "   - *'group_age'* <br><br>\n",
    " - Performed feature selection to find top features:\n",
    " \n",
    "    Original\n",
    "    - Area\n",
    "    - Bedrooms\n",
    "    - Bathrooms\n",
    "    - Age\n",
    "\n",
    "    Engineered\n",
    "    - total_rooms\n",
    "    - bdrm_area_ratio\n",
    "    - bath_bdrm_ratio<br><br>\n",
    " - The final model significantly out performed the baseline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\">Recommendations</font><br><br>\n",
    "\n",
    "With additional time to improve the model, I would:\n",
    " - Include additional features from the zillow data.\n",
    " - Create additional model to handle outliers.\n",
    " - Create location based cluster features.\n",
    " - Dive into hyperparameter to improve the models."
   ]
  }
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