Mumbai-House-Prediction-Project

Introduction:

Welcome to the Mumbai House Prediction Project In this repository, we utilize data analysis and machine learning techniques to forecast housing trends in the bustling city of Mumbai. Our aim is to empower individuals with valuable insights to make informed decisions in the dynamic real estate market of Mumbai.

Motivation:

The motivation behind initiating this project stems from the necessity to address the challenges individuals face when navigating the complex real estate landscape in Mumbai. With rapidly changing market conditions and a plethora of factors influencing property prices, prospective buyers, sellers, and investors often find it daunting to make well-informed decisions. By leveraging advanced data analysis and machine learning algorithms, we seek to provide a reliable tool that can forecast housing trends accurately. Our goal is to empower individuals with the knowledge needed to navigate the Mumbai real estate market confidently, ultimately aiding in making sound investment choices and facilitating smoother transactions.

Here I used Mumbai Housing dataset which has the following features:

Price, Area, Location, No. of Bedrooms, New/Resale, Gymnasium, Lift, Available, Car Parking, Maintenance, Staff, 24x7 Security, Children, Clubhouse, Intercom, Landscaped Gardens, Indoor Games, Gas Connection, Jogging Track, Swimming Pool.

Model Building and Features:

I utilized the Decision Tree Regreesor from Scikit-learn to construct the predictive model. The model is designed to estimate house prices in Mumbai based on the features: Area, Number of Bedrooms, and Location.
The Target feature for prediction is Price.

Tools Utilized:

In this project, I employed the following tools and technologies to develop and deploy the predictive model:

• Programming Language: Python
  
• Data Manipulation: Numpy and Pandas for efficient data cleaning and preprocessing
  
• Data Visualization: Matplotlib for insightful visualizations of the dataset
  
• Machine Learning:Sklearn library for building and training the Decision Tree Regressor model
  
• Development Environment: Utilized Jupyter Notebook, WebStorm, and DataSpell as IDEs for coding and analysis
  
• Server-side Scripting: Python Flask for setting up the HTTP server
  
• User Interface: Designed the UI using HTML, CSS, and Javascript for a seamless user experience.
  

Output:

Future Updates:

1. Enhanced User Interface: Improve the user interface by adding interactive elements and intuitive design for a smoother user experience.
   

2. Automated Data Updates: Implement automated data fetching and updating mechanisms to ensure that the model stays up-to-date with the latest housing market trends.