brianotiodhiambo-source/analystlab-week4-supervised-learning

Week 4 AnalystLab Africa Data Science Internship Project: Supervised Learning using Linear Regression for House Price Prediction and Logistic Regression for Titanic Survival Prediction. # Week 4: Supervised Learning ## AnalystLab Africa Data Science Internship ### Project Overview This project was completed as part of the AnalystLab Africa Data Science Internship Program. The objective was to apply supervised machine learning techniques to real-world datasets by building and evaluating both regression and classification models. --- ## Datasets Used ### 1. Housing Dataset Objective: Predict house prices using housing characteristics such as area, bedrooms, bathrooms, parking spaces, and furnishing status. ### 2. Titanic Dataset Objective: Predict passenger survival using demographic and travel-related information such as age, gender, passenger class, and fare. --- ## Technologies Used * Python * Pandas * NumPy * Matplotlib * Seaborn * Scikit-learn * Jupyter Notebook --- ## Project Workflow ### Housing Dataset – Linear Regression 1. Data Loading 2. Data Inspection 3. Data Preprocessing 4. Feature Encoding 5. Train-Test Split 6. Model Training using Linear Regression 7. Predictions 8. Model Evaluation #### Results * RMSE: 1,324,506.96 * R² Score: 0.653 #### Interpretation The model explained approximately 65.3% of the variation in house prices, demonstrating moderate predictive performance. --- ### Titanic Dataset – Logistic Regression 1. Data Loading 2. Data Cleaning 3. Handling Missing Values 4. Feature Encoding 5. Train-Test Split 6. Model Training using Logistic Regression 7. Predictions 8. Model Evaluation #### Results * Accuracy Score: 81.01% #### Confusion Matrix | Actual / Predicted | 0 | 1 | | ------------------ | -- | -- | | 0 | 90 | 15 | | 1 | 19 | 55 | #### Interpretation The Logistic Regression model achieved an accuracy of 81.01%, indicating strong predictive performance in identifying passenger survival outcomes. --- ## Key Insights * House area, bathrooms, and parking spaces signifi …