Bangalore_House_Price_Prediction

Introduction

Creating a Machine Learning model to predict the home prices in Bangalore, India. We are going to use the dataset from Kaggle.com.

Below data science concepts are used in this project

• Data loading and cleaning
• Outlier detection and removal
• Feature engineering
• Dimensionality reduction
• Gridsearchcv for hyperparameter tunning
• K fold cross validation

Technology and tools used in this project

• Python
• Numpy and Pandas for data cleaning
• Matplotlib for data visualization
• Sklearn for model building

Steps

• Step#1: Import the required libraries

• Step#2: Load the data

• Step#3: Understand the data

    - drop unnecessary columns
  

• Step#4: Data Cleaning

    - Check for na values
    - Verify unique values of each column
    - Make sure values are correct (eg. 23 BHK home with 2000 Sqrft size is worng)        
    - Feature Engineering        
    - Dimesionality Reduction
    - Outlier removal using domain knowledge (2bhk price < 3bhk price, size per bhk >= 300 sqft)
    - Outlier removal using standard eviation and mean
    - One Hot encoding
  

• Step#5: Build Machine Learning Model

• Step#6: Testing The model

Dataset Reference##

• Bengaluru House price data
• I have also uploaed the csv file in this repository Bengaluru_House_Data.csv

Reference codebasics