This github repository contains practice assignments on Python !
Project Title: Predicting House Prices
Project Overview:
The goal of this project is to predict house prices based on various features such as square footage, number of bedrooms and bathrooms, location, and other relevant factors. This is a classic regression problem in data science.
Project Workflow:
Exploratory Data Analysis (EDA):
Objective: Gain insights into the dataset, understand its structure, and identify key patterns and relationships between variables.
Tasks: Visualize distributions of numerical features, explore correlations between features and target variable (house prices), detect outliers, and assess data quality.
Data Preprocessing:
Objective: Prepare the data for modeling by handling missing values, scaling numerical features, encoding categorical variables, and addressing outliers identified during EDA.
Tasks: Impute missing values using appropriate techniques (mean, median, etc.), normalize or standardize numerical features to ensure all variables contribute equally to the analysis, and encode categorical variables using methods like one-hot encoding or label encoding.
Feature Engineering:
Objective: Enhance model performance by creating new features or transforming existing ones that better capture relationships within the data.
Tasks: Generate new features such as total area (combining square footage of house and lot size), create interaction terms between correlated variables, and apply transformations (logarithmic, polynomial) to numerical features to meet regression assumptions and improve predictive accurace.
Model Building:
Objective: Develop and train regression models capable of predicting house prices based on the processed dataset.
Tasks: Implement various regression algorithms such as Linear Regression, Decision Trees, Random Forest, Gradient Boosting, and possibly ensemble methods to build robust models. Utilize techniques like cross-validation to select the best-performing model and optimize hyperparameters.
Model Evaluation:
Objective: Assess the performance of trained models to ensure they generalize well to unseen data and provide accurate predictions.
Tasks: Evaluate models using metrics suitable for regression tasks such as RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R-squared (coefficient of determination). Compare models to identify the most effective in predicting house prices accurately.
Deployment and Reporting:
Objective: Deploy the best-performing model for real-world use and prepare comprehensive reports documenting the project's findings, insights, and recommendations.
Tasks: Save the final model, create documentation detailing the data preprocessing steps, feature engineering techniques, model selection rationale, and performance metrics. Present results in a clear and understandable format for stakeholders.
Additional Considerations:
Version Control: Utilize Git for version control to manage project iterations, track changes, collaborate with team members, and maintain a detailed project history.
Documentation: Maintain clear and organized documentation throughout the project, including Jupyter notebooks for EDA, model development, and evaluation, a README file outlining project details and setup instructions, and a final report summarizing key findings, challenges faced, and future recommendations.
This structured approach ensures a systematic and thorough exploration of the dataset, effective preprocessing to enhance model performance, rigorous model building and evaluation, and clear documentation to support reproducibility and knowledge transfer.