InternnCraft-House-Price-Prediction

Project Submission Guidelines

1. Time Management • Ensure you adhere strictly to the deadlines. Timeliness is crucial.
2. Proper Documentation • Along with your project submission, prepare and submit proper documentation detailing your project. This documentation should include: o Project Overview: A brief description of your project. o Objectives: The main goals and objectives of your project. o Methodology: The approach and methods you used to complete the project. o Challenges: Any challenges or hurdles you faced and how you overcame them. o Conclusion: The final outcome and any recommendations or future steps.
3. Originality • Your project must be original and not copied from any source. Plagiarism will result in the cancellation of your project submission. Note • Regularly update your LinkedIn profile with your progress and achievements, as it is crucial for your career. INTERNCRAFT TASK#1 Assigned:15-07-2024 Submission date: 30-07- 2024 House Price Analysis and Prediction Objective: Analyze a dataset of house prices to understand pricing factors, identify outliers, and develop a model for future price prediction. Tasks:
4. Data Cleaning and Exploration: o Clean the data: Identify and handle missing values, inconsistencies, and outliers. o Explore the data: Analyze the distribution of house prices and other features. Identify potential relationships between features and price using visualizations (scatter plots, box plots, etc.).
5. Feature Engineering: o Create new features that might be relevant for price prediction (e.g., age of the house, number of bedrooms per floor). o Consider encoding categorical features (e.g., location) into numerical values suitable for modeling.
6. Outlier Analysis: o Identify houses with significantly higher or lower prices compared to similar properties. o Investigate the reasons for these outliers. Are there any specific features or combinations of features that contribute to the outliers?
7. Predictive Modeling: o Train a machine learning model to predict house prices based on the available features. Popular choices for this task include linear regression, random forest, or gradient boosting. o Evaluate the performance of the model using appropriate metrics (e.g., mean squared error, R-squared).
8. Future Price Prediction: o Use the trained model to predict future house prices based on hypothetical scenarios (e.g., what would be the price of a house with specific characteristics in a particular location?).
9. Report and Recommendations: o Prepare a report summarizing your findings, including: ▪ Data exploration results (key insights from visualizations) ▪ Feature engineering techniques used ▪ Outlier analysis (identification and explanation) ▪ Model selection and evaluation results ▪ Future price prediction examples ▪ Recommendations for further analysis or data collection (if applicable) Deliverables: • A well-documented script containing your data cleaning, exploration, feature engineering, modeling, and prediction code. • A clear and concise report summarizing your findings and recommendations. Evaluation: • Your work will be evaluated based on the following criteria: o Completeness of tasks o Data analysis skills (cleaning, exploration, visualization) o Understanding of feature engineering concepts o Ability to build and evaluate a machine learning model o Quality and clarity of reporting