937 lines
30 KiB
Plaintext
937 lines
30 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "i6sPnhMH0raw"
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},
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"source": [
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"# House Price Prediction\n",
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"\n",
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"Think of finding the perfect house as a complex journey involving negotiations, research, and decision-making. Now, imagine having a smart guide that helps you navigate through this maze by analyzing data and predicting outcomes. Linear regression is that guide. In this tutorial, we'll explore how linear regression helps us understand and predict relationships in data, just like finding the ideal house by matching features with price. Let's get started and discover how this powerful tool can simplify your data-driven decisions!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "huwdh4Es3EZ2"
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},
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"source": [
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"## Setup\n",
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"\n",
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"The House Price Prediction Dataset contains 13 features\n",
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"\n",
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"| # | Column Name | Description |\n",
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"|----|----------------|---------------------------------------------------------------------|\n",
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"| 1 | Id | To count the records. |\n",
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"| 2 | MSSubClass | Identifies the type of dwelling involved in the sale. |\n",
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"| 3 | MSZoning | Identifies the general zoning classification of the sale. |\n",
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"| 4 | LotArea | Lot size in square feet. |\n",
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"| 5 | LotConfig | Configuration of the lot |\n",
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"| 6 | BldgType | Type of dwelling |\n",
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"| 7 | OverallCond | Rates the overall condition of the house |\n",
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"| 8 | YearBuilt | Original construction year |\n",
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"| 9 | YearRemodAdd | Remodel date (same as construction date if no remodeling or additions). |\n",
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"| 10 | Exterior1st | Exterior covering on house |\n",
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"| 11 | BsmtFinSF2 | Type 2 finished square feet. |\n",
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"| 12 | TotalBsmtSF | Total square feet of basement area |\n",
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"| 13 | SalePrice | To be predicted |\n",
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"\n",
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"\n",
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"Run the cell below to download dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "W5Y7ZPKz0nf8",
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"outputId": "786a4293-3f63-4065-c005-4d5b11d8c15b"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--2024-08-23 11:26:23-- https://docs.google.com/spreadsheets/d/1caaR9pT24GNmq3rDQpMiIMJrmiTGarbs/export?format=csv&id=1caaR9pT24GNmq3rDQpMiIMJrmiTGarbs&gid=1150341366\n",
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"Resolving docs.google.com (docs.google.com)... 172.217.164.14, 2607:f8b0:4025:803::200e\n",
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"Connecting to docs.google.com (docs.google.com)|172.217.164.14|:443... connected.\n",
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"HTTP request sent, awaiting response... 307 Temporary Redirect\n",
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"Location: https://doc-08-30-sheets.googleusercontent.com/export/54bogvaave6cua4cdnls17ksc4/pq9qv18vh410aflmm0u38bfc4o/1724412380000/115253717745408081083/*/1caaR9pT24GNmq3rDQpMiIMJrmiTGarbs?format=csv&id=1caaR9pT24GNmq3rDQpMiIMJrmiTGarbs&gid=1150341366 [following]\n",
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"Warning: wildcards not supported in HTTP.\n",
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"--2024-08-23 11:26:24-- https://doc-08-30-sheets.googleusercontent.com/export/54bogvaave6cua4cdnls17ksc4/pq9qv18vh410aflmm0u38bfc4o/1724412380000/115253717745408081083/*/1caaR9pT24GNmq3rDQpMiIMJrmiTGarbs?format=csv&id=1caaR9pT24GNmq3rDQpMiIMJrmiTGarbs&gid=1150341366\n",
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"Resolving doc-08-30-sheets.googleusercontent.com (doc-08-30-sheets.googleusercontent.com)... 172.217.12.1, 2607:f8b0:4025:815::2001\n",
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"Connecting to doc-08-30-sheets.googleusercontent.com (doc-08-30-sheets.googleusercontent.com)|172.217.12.1|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: unspecified [text/csv]\n",
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"Saving to: ‘dataset.csv’\n",
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"\n",
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"dataset.csv [ <=> ] 171.41K --.-KB/s in 0.03s \n",
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"\n",
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"2024-08-23 11:26:24 (5.15 MB/s) - ‘dataset.csv’ saved [175524]\n",
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"\n"
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]
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}
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],
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"source": [
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"!wget -O dataset.csv \"https://docs.google.com/spreadsheets/d/1caaR9pT24GNmq3rDQpMiIMJrmiTGarbs/export?format=csv&id=1caaR9pT24GNmq3rDQpMiIMJrmiTGarbs&gid=1150341366\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "-r572AXndHZn"
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},
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"source": [
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"### Importing libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "Vrb6A7dQdGK2"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from sklearn.preprocessing import OneHotEncoder\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"from sklearn.model_selection import train_test_split\n",
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"import numpy as np\n",
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"from sklearn.metrics import mean_squared_error\n",
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"from sklearn.metrics import mean_absolute_error\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "KLdgiCGJ9dN-"
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},
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"source": [
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"### Create DataFrame"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "NhdMHsM4831s"
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},
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"outputs": [],
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"source": [
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"file_path = '/content/dataset.csv'\n",
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"df = pd.read_csv(file_path)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "KtYExjQRHBQo"
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},
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"source": [
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"# Preprocessing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "x7n5GmAWwoO_"
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},
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"outputs": [],
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"source": [
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"df.drop(['Id'],axis=1,inplace=True)\n",
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"df['SalePrice'].fillna(df['SalePrice'].mean(), inplace=True)\n",
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"df_copy=df.copy()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "2LrDmZryG9Pm"
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},
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"outputs": [],
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"source": [
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"new_data = df_copy.dropna()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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||
},
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"id": "Q0PeWhNYHTdl",
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||
"outputId": "f3988361-0a4c-4155-dbf2-d852e1d90ccd"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Categorical variables:\n",
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"['MSZoning', 'LotConfig', 'BldgType', 'Exterior1st']\n",
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"No. of. categorical features: 4\n"
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]
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}
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],
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"source": [
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"s = (new_data.dtypes == 'object')\n",
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"object_cols = list(s[s].index)\n",
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"print(\"Categorical variables:\")\n",
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"print(object_cols)\n",
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"print('No. of. categorical features: ',\n",
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"\tlen(object_cols))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "qzkrtVtEd2ab"
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},
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"source": [
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"### One Hot Encoding"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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||
"colab": {
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||
"base_uri": "https://localhost:8080/",
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"height": 307
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},
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"id": "HwH4nB9QILS8",
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"outputId": "a1ccab90-5d6d-4c09-b5b8-c5d9a9b831a8"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
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" warnings.warn(\n"
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]
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},
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{
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"data": {
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||
"application/vnd.google.colaboratory.intrinsic+json": {
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||
"type": "dataframe",
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||
"variable_name": "df_final"
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},
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"text/html": [
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"\n",
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" <div id=\"df-0f237bc8-b111-4549-9ccd-7f7bc7b2cbcf\" class=\"colab-df-container\">\n",
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||
" <div>\n",
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||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
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||
" }\n",
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||
"\n",
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||
" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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||
" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
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||
" <th>MSSubClass</th>\n",
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||
" <th>LotArea</th>\n",
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||
" <th>OverallCond</th>\n",
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||
" <th>YearBuilt</th>\n",
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||
" <th>YearRemodAdd</th>\n",
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" <th>BsmtFinSF2</th>\n",
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" <th>TotalBsmtSF</th>\n",
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" <th>SalePrice</th>\n",
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||
" <th>MSZoning_C (all)</th>\n",
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||
" <th>MSZoning_FV</th>\n",
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||
" <th>...</th>\n",
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||
" <th>Exterior1st_CemntBd</th>\n",
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||
" <th>Exterior1st_HdBoard</th>\n",
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||
" <th>Exterior1st_ImStucc</th>\n",
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||
" <th>Exterior1st_MetalSd</th>\n",
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||
" <th>Exterior1st_Plywood</th>\n",
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" <th>Exterior1st_Stone</th>\n",
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||
" <th>Exterior1st_Stucco</th>\n",
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||
" <th>Exterior1st_VinylSd</th>\n",
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||
" <th>Exterior1st_Wd Sdng</th>\n",
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||
" <th>Exterior1st_WdShing</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>60</td>\n",
|
||
" <td>8450</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2003</td>\n",
|
||
" <td>2003</td>\n",
|
||
" <td>0.0</td>\n",
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||
" <td>856.0</td>\n",
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" <td>208500.0</td>\n",
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" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
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||
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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||
" <td>1.0</td>\n",
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||
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
|
||
" <th>1</th>\n",
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||
" <td>20</td>\n",
|
||
" <td>9600</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>1976</td>\n",
|
||
" <td>1976</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1262.0</td>\n",
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||
" <td>181500.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>...</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
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||
" <tr>\n",
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||
" <th>2</th>\n",
|
||
" <td>60</td>\n",
|
||
" <td>11250</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2001</td>\n",
|
||
" <td>2002</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>920.0</td>\n",
|
||
" <td>223500.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>...</td>\n",
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||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
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||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>70</td>\n",
|
||
" <td>9550</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1915</td>\n",
|
||
" <td>1970</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>756.0</td>\n",
|
||
" <td>140000.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>...</td>\n",
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||
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||
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|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
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|
||
" <td>1.0</td>\n",
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||
" <td>0.0</td>\n",
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||
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|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>60</td>\n",
|
||
" <td>14260</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2000</td>\n",
|
||
" <td>2000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1145.0</td>\n",
|
||
" <td>250000.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
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||
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||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" <td>0.0</td>\n",
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||
" </tr>\n",
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||
" </tbody>\n",
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||
"</table>\n",
|
||
"<p>5 rows × 38 columns</p>\n",
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||
"</div>\n",
|
||
" <div class=\"colab-df-buttons\">\n",
|
||
"\n",
|
||
" <div class=\"colab-df-container\">\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f237bc8-b111-4549-9ccd-7f7bc7b2cbcf')\"\n",
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||
" title=\"Convert this dataframe to an interactive table.\"\n",
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||
" style=\"display:none;\">\n",
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"\n",
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" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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|
||
" </svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" gap: 12px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
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||
" cursor: pointer;\n",
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" display: none;\n",
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" fill: #1967D2;\n",
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" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-buttons div {\n",
|
||
" margin-bottom: 4px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-0f237bc8-b111-4549-9ccd-7f7bc7b2cbcf button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-0f237bc8-b111-4549-9ccd-7f7bc7b2cbcf');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
"\n",
|
||
"<div id=\"df-32d495b9-a579-4bb8-8725-2243d77acadb\">\n",
|
||
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-32d495b9-a579-4bb8-8725-2243d77acadb')\"\n",
|
||
" title=\"Suggest charts\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <g>\n",
|
||
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
||
" </g>\n",
|
||
"</svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
"<style>\n",
|
||
" .colab-df-quickchart {\n",
|
||
" --bg-color: #E8F0FE;\n",
|
||
" --fill-color: #1967D2;\n",
|
||
" --hover-bg-color: #E2EBFA;\n",
|
||
" --hover-fill-color: #174EA6;\n",
|
||
" --disabled-fill-color: #AAA;\n",
|
||
" --disabled-bg-color: #DDD;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-quickchart {\n",
|
||
" --bg-color: #3B4455;\n",
|
||
" --fill-color: #D2E3FC;\n",
|
||
" --hover-bg-color: #434B5C;\n",
|
||
" --hover-fill-color: #FFFFFF;\n",
|
||
" --disabled-bg-color: #3B4455;\n",
|
||
" --disabled-fill-color: #666;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart {\n",
|
||
" background-color: var(--bg-color);\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: var(--fill-color);\n",
|
||
" height: 32px;\n",
|
||
" padding: 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart:hover {\n",
|
||
" background-color: var(--hover-bg-color);\n",
|
||
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: var(--button-hover-fill-color);\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart-complete:disabled,\n",
|
||
" .colab-df-quickchart-complete:disabled:hover {\n",
|
||
" background-color: var(--disabled-bg-color);\n",
|
||
" fill: var(--disabled-fill-color);\n",
|
||
" box-shadow: none;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-spinner {\n",
|
||
" border: 2px solid var(--fill-color);\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" animation:\n",
|
||
" spin 1s steps(1) infinite;\n",
|
||
" }\n",
|
||
"\n",
|
||
" @keyframes spin {\n",
|
||
" 0% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 20% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 30% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 40% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 60% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 80% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 90% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" async function quickchart(key) {\n",
|
||
" const quickchartButtonEl =\n",
|
||
" document.querySelector('#' + key + ' button');\n",
|
||
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
||
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
||
" try {\n",
|
||
" const charts = await google.colab.kernel.invokeFunction(\n",
|
||
" 'suggestCharts', [key], {});\n",
|
||
" } catch (error) {\n",
|
||
" console.error('Error during call to suggestCharts:', error);\n",
|
||
" }\n",
|
||
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
||
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
||
" }\n",
|
||
" (() => {\n",
|
||
" let quickchartButtonEl =\n",
|
||
" document.querySelector('#df-32d495b9-a579-4bb8-8725-2243d77acadb button');\n",
|
||
" quickchartButtonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
"</div>\n",
|
||
"\n",
|
||
" </div>\n",
|
||
" </div>\n"
|
||
],
|
||
"text/plain": [
|
||
" MSSubClass LotArea OverallCond YearBuilt YearRemodAdd BsmtFinSF2 \\\n",
|
||
"0 60 8450 5 2003 2003 0.0 \n",
|
||
"1 20 9600 8 1976 1976 0.0 \n",
|
||
"2 60 11250 5 2001 2002 0.0 \n",
|
||
"3 70 9550 5 1915 1970 0.0 \n",
|
||
"4 60 14260 5 2000 2000 0.0 \n",
|
||
"\n",
|
||
" TotalBsmtSF SalePrice MSZoning_C (all) MSZoning_FV ... \\\n",
|
||
"0 856.0 208500.0 0.0 0.0 ... \n",
|
||
"1 1262.0 181500.0 0.0 0.0 ... \n",
|
||
"2 920.0 223500.0 0.0 0.0 ... \n",
|
||
"3 756.0 140000.0 0.0 0.0 ... \n",
|
||
"4 1145.0 250000.0 0.0 0.0 ... \n",
|
||
"\n",
|
||
" Exterior1st_CemntBd Exterior1st_HdBoard Exterior1st_ImStucc \\\n",
|
||
"0 0.0 0.0 0.0 \n",
|
||
"1 0.0 0.0 0.0 \n",
|
||
"2 0.0 0.0 0.0 \n",
|
||
"3 0.0 0.0 0.0 \n",
|
||
"4 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
" Exterior1st_MetalSd Exterior1st_Plywood Exterior1st_Stone \\\n",
|
||
"0 0.0 0.0 0.0 \n",
|
||
"1 1.0 0.0 0.0 \n",
|
||
"2 0.0 0.0 0.0 \n",
|
||
"3 0.0 0.0 0.0 \n",
|
||
"4 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
" Exterior1st_Stucco Exterior1st_VinylSd Exterior1st_Wd Sdng \\\n",
|
||
"0 0.0 1.0 0.0 \n",
|
||
"1 0.0 0.0 0.0 \n",
|
||
"2 0.0 1.0 0.0 \n",
|
||
"3 0.0 0.0 1.0 \n",
|
||
"4 0.0 1.0 0.0 \n",
|
||
"\n",
|
||
" Exterior1st_WdShing \n",
|
||
"0 0.0 \n",
|
||
"1 0.0 \n",
|
||
"2 0.0 \n",
|
||
"3 0.0 \n",
|
||
"4 0.0 \n",
|
||
"\n",
|
||
"[5 rows x 38 columns]"
|
||
]
|
||
},
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"OH_encoder = OneHotEncoder(sparse=False)\n",
|
||
"OH_cols = pd.DataFrame(OH_encoder.fit_transform(new_data[object_cols]))\n",
|
||
"OH_cols.index = new_data.index\n",
|
||
"OH_cols.columns = OH_encoder.get_feature_names_out()\n",
|
||
"df_final = new_data.drop(object_cols, axis=1)\n",
|
||
"df_final = pd.concat([df_final, OH_cols], axis=1)\n",
|
||
"df_final.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "eZDFB6FZv1k-"
|
||
},
|
||
"source": [
|
||
"### Scaling"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "Ve81LFh9wEs0"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"scaler = MinMaxScaler()\n",
|
||
"df_normalized = pd.DataFrame(scaler.fit_transform(df_final), columns=df_final.columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "TCfu66R_cyAJ"
|
||
},
|
||
"source": [
|
||
"# Data splitting"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "wiiNfRQRgR1G"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X, y = df_normalized.loc[:, df_normalized.columns != 'SalePrice'], df_normalized[['SalePrice']]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "gMCC4I4bgjf2"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "o5k0PNG_pMGY",
|
||
"outputId": "59927321-5524-4bc6-cf99-b579566c6527"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"((2330, 37), (583, 37), (2330, 1), (583, 1))"
|
||
]
|
||
},
|
||
"execution_count": 47,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X_train.shape, X_test.shape, y_train.shape, y_test.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "yKhUdAyntrv8",
|
||
"outputId": "d60ccb04-1f08-4cf2-8503-0f5d980322c3"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(755000.0, 34900.0)"
|
||
]
|
||
},
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"label_max, label_min = df[\"SalePrice\"].max(), df[\"SalePrice\"].min()\n",
|
||
"label_max,label_min"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "7c7wVOvwtk7V"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"y_reg_test = y_test * (label_max - label_min) + label_min\n",
|
||
"feature_names=X_train.columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "egogzbyuca1N"
|
||
},
|
||
"source": [
|
||
"# Model Building"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "jC2CYGELsB-G"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def init_params(n_features):\n",
|
||
" w = np.zeros((n_features, 1)) # no of weights should be 1 + no. of featuers, to account for the bias term\n",
|
||
" b = 0\n",
|
||
" return w,b\n",
|
||
"\n",
|
||
"def log_likelihood(X, y,w,b):\n",
|
||
" # Assume variance = 1\n",
|
||
" N = len(y)\n",
|
||
" y_pred = np.dot(X, w) + b\n",
|
||
" residual = y - y_pred\n",
|
||
" log_likelihood = -0.5 * N * np.log(2 * np.pi) - 0.5 * np.sum(residual ** 2)\n",
|
||
" return log_likelihood\n",
|
||
"\n",
|
||
"def update(X, y,w,b,n_iterations=400,learning_rate=0.001):\n",
|
||
" # Assume variance = 1\n",
|
||
" N = len(y)\n",
|
||
" for i in range(n_iterations):\n",
|
||
" y_pred = np.dot(X, w) + b\n",
|
||
"\n",
|
||
" dw = np.dot(X.T, (y - y_pred)) / N\n",
|
||
" db = np.sum(y - y_pred) / N\n",
|
||
"\n",
|
||
" w = w - learning_rate * dw\n",
|
||
" b = b - learning_rate * db\n",
|
||
"\n",
|
||
" return w,b\n",
|
||
"\n",
|
||
"def train_model(X, y):\n",
|
||
"\n",
|
||
" w,b=init_params(X.shape[1])\n",
|
||
"\n",
|
||
" w,b=update(X, y,w,b)\n",
|
||
"\n",
|
||
" return w,b\n",
|
||
"\n",
|
||
"def predict(X,w,b):\n",
|
||
" return np.dot(X, w) + b"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "3OPBEw2KrAH7"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"W, b = train_model(X_train.values,y_train.values)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "GfLO0M_EvFxU"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"y_pred = predict(X_test,W,b)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "jYP5Zc18vNqm"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"y_pred=y_pred * (label_max - label_min) + label_min\n",
|
||
"mae=mean_absolute_error(y_reg_test,y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "9XYUL6hpvYTA",
|
||
"outputId": "422ec562-f641-48d1-ddb6-4211fefd66ad"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"858342.4210579091"
|
||
]
|
||
},
|
||
"execution_count": 54,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"mae"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "PxShUdVuv5jn"
|
||
},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"provenance": []
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.9"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|