[Code Addition Request]: Add House Price Prediction Project

[sanchitc05]

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Guidelines

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• I have the link to my latest merged PR

Latest Merged PR Link

ayush-that/FinVeda#2526

Project Description

This project aims to predict house prices using machine learning regression models. We will leverage features like square footage, number of bedrooms, and location to build accurate predictive models.

Proposed Approach:

1. Data Collection and Preprocessing:

   • Gather relevant housing data from reliable sources.
   • Clean and preprocess the data to handle missing values, outliers, and inconsistencies.
   • Feature engineering: Create new features or transform existing ones to improve model performance.

2. Exploratory Data Analysis (EDA):

   • Visualize data distributions and correlations between features.
   • Identify potential patterns and insights.

3. Model Selection and Training:

   • Experiment with regression algorithms like Linear Regression, Decision Trees, Random Forest, and XGBoost.
   • Train and evaluate models using appropriate metrics (e.g., Mean Squared Error, Mean Absolute Error, R-squared).

4. Model Evaluation and Fine-Tuning:

   • Assess model performance on a validation set.
   • Fine-tune hyperparameters to optimize results.

5. Deployment (Optional):

   • Create a web application or API to deploy the model for real-time predictions.

Required Skills:

• Python Programming
• Data cleaning and preprocessing
• Exploratory data analysis
• Machine learning concepts (regression)
• Model evaluation and tuning
• Data visualization

Additional Considerations:

• Incorporate advanced techniques like feature engineering and regularization.
• Consider using ensemble methods for improved accuracy.
• Explore the impact of location-based features on house prices.

By completing this project, you will gain hands-on experience in data science, machine learning, and real-world problem-solving.

Full Name

Sanchit Chauhan

Participant Role

GSSOC

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