This readme file provides a comprehensive overview of the projects completed during the internship at Prodigy Infotech. Each project focused on different aspects of data science and machine learning. Below, you will find details about each task, the approach taken, and the directory structure for easy navigation.
Predicting housing prices based on various features using advanced linear regression.
- Data Exploration: Analyzed dataset to understand features and distribution.
- Data Preprocessing: Handled missing values, outliers, and performed feature scaling.
- Advanced Linear Regression: Implemented advanced linear regression models considering regularization techniques (e.g., Ridge, Lasso) to improve accuracy.
- Model Evaluation: Evaluated models using metrics like Mean Squared Error (MSE) and R-squared.
Segmenting customers based on their behavior using KMeans clustering.
- Data Exploration: Explored customer data to identify patterns.
- Data Preprocessing: Cleaned and scaled data for clustering.
- KMeans Clustering: Utilized KMeans algorithm to group customers based on common characteristics.
- Interpretation: Analyzed clusters to derive meaningful insights.
Classifying images as either cat or dog using Support Vector Machines (SVM).
- Data Preparation: Organized and labeled a dataset of cat and dog images.
- Feature Extraction: Extracted relevant features from images.
- SVM Model Training: Trained SVM classifier for image classification.
- Model Evaluation: Assessed model performance using accuracy and confusion matrix.
Predicting calorie content in food items based on various factors.
- Data Collection: Gathered a comprehensive dataset with food features.
- Data Preprocessing: Cleaned and standardized data for modeling.
- Fine-tuning: Experimented with hyperparameter tuning to optimize the model.