| Sl. No. | Title | Mini Project | Major Project (Yes/No) |
|---|---|---|---|
| 1 | Customer Churn Prediction | Yes | No |
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The Customer Churn Prediction project focuses on using machine learning algorithms to predict if a customer will stop using a service. The project involves data preprocessing, feature selection, and model training using algorithms such as Logistic Regression, Random Forest, and XGBoost. Evaluation metrics like accuracy, precision, recall, and F1-score are used to assess the model’s performance. The outcome helps businesses retain customers by identifying and addressing churn risks proactively. |
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| 2 | House Price Prediction | No | Yes |
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This project aims to predict the selling prices of houses using various features such as location, size, number of rooms, and more. Techniques like Linear Regression, Decision Trees, and Gradient Boosting are utilized to build predictive models. The project involves data cleaning, feature engineering, and model evaluation using metrics like Mean Absolute Error and Root Mean Squared Error. |
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| 3 | Sentiment Analysis on Social Media | Yes | No |
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The Sentiment Analysis project focuses on extracting and analyzing the sentiment of text data from social media platforms. Techniques like Natural Language Processing (NLP), TextBlob, and machine learning classifiers such as Naive Bayes and Support Vector Machines are used. The project aims to classify text as positive, negative, or neutral, providing insights into public opinion and trends. |
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| 4 | Stock Market Prediction | No | Yes |
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This project involves predicting stock prices using historical data and machine learning techniques. Methods such as Time Series Analysis, ARIMA models, and LSTM (Long Short-Term Memory) neural networks are employed to forecast future stock prices. The project helps investors make informed decisions by analyzing trends and patterns in stock market data. |
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| 5 | Image Classification with CNN | No | Yes |
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The Image Classification project uses Convolutional Neural Networks (CNN) to classify images into different categories. The project involves data preprocessing, augmentation, and training a CNN model using frameworks like TensorFlow or PyTorch. Applications include object recognition, medical image analysis, and automated image tagging. |
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| 6 | Recommendation System | Yes | No |
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This project focuses on building a recommendation system to suggest products, movies, or other items to users based on their preferences. Techniques like Collaborative Filtering, Content-Based Filtering, and Hybrid Methods are utilized. The project aims to enhance user experience by providing personalized recommendations, increasing engagement and satisfaction. |
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