Price Prediction Model

This project focuses on predicting housing prices in Bangalore using the Bangalore Housing Price dataset obtained from Kaggle.

TechStack Used is:-

1.Pandas
2.Sci-kit learn
3.Numpy
4.Flask
5.HTML
6.CSS

Overview

The project follows these main steps:

1. Data Preprocessing: The dataset undergoes preprocessing to handle discrepancies such as missing values and duplicate entries. Missing values are either replaced by the average values or the highest frequency value.
2. Feature Engineering: Feature engineering is performed to enhance the data's suitability for model training, ultimately improving prediction accuracy.
3. Data Splitting: The dataset is split into training and testing datasets. The training dataset is utilized to train the model, while the testing dataset is used for model evaluation.
4. Model Training and Evaluation: The project employs both Linear Regression and Ridge Regression models for training. The accuracy levels of both models are compared to determine the most suitable model for predicting housing prices.