Mini Project List

1. Predicting House Prices

Objective: Predict house prices using the Boston Housing or any real estate dataset.
Key Techniques: Regression, Feature Engineering, Data Visualization
Skills: Data Preprocessing, Random Forest Regressor, Linear Regression

2. Iris Flower Classification

Objective: Classify iris flower species based on features like sepal length, petal width, etc.
Key Techniques: Classification, Decision Trees, SVM, k-NN
Skills: Supervised Learning, Data Splitting, Model Evaluation

3. Sentiment Analysis on Tweets

Objective: Classify the sentiment of tweets (positive, neutral, or negative).
Key Techniques: Natural Language Processing (NLP), Text Mining, Logistic Regression
Skills: Tokenization, TF-IDF, Word Embeddings, Sentiment Classification

4. Customer Segmentation using Clustering

Objective: Segment customers into different groups based on purchasing behavior or demographic information.
Key Techniques: Clustering (K-Means, DBSCAN), Dimensionality Reduction (PCA)
Skills: Unsupervised Learning, Data Visualization, Feature Scaling

5. Predicting Employee Attrition

Objective: Predict whether an employee will leave the company or not using HR datasets.
Key Techniques: Classification (Decision Trees, Random Forests), Logistic Regression
Skills: Data Imputation, Model Tuning, Confusion Matrix Analysis

6. Stock Price Prediction

Objective: Predict stock prices using historical stock market data.
Key Techniques: Time Series Forecasting, ARIMA, LSTM (Long Short-Term Memory)
Skills: Data Analysis, Time Series Decomposition, Feature Engineering

7. Recommender System for Movies

Objective: Build a recommendation engine for suggesting movies to users.
Key Techniques: Collaborative Filtering, Content-Based Filtering, Matrix Factorization
Skills: Data Wrangling, Model-based Collaborative Filtering, Data Visualization

8. Credit Card Fraud Detection

Objective: Identify fraudulent credit card transactions using an imbalanced dataset.
Key Techniques: Classification, Anomaly Detection, SMOTE (Synthetic Minority Over-sampling Technique)
Skills: Data Balancing, Precision-Recall Curves, Random Forest Classifier

9. Breast Cancer Prediction

Objective: Predict whether a tumor is malignant or benign using the Breast Cancer dataset.
Key Techniques: Logistic Regression, Decision Trees, Random Forest
Skills: Model Evaluation, Cross-Validation, ROC-AUC Analysis

10. Forecasting Sales for Retail

Objective: Forecast sales for a retail store using historical data.
Key Techniques: Time Series Analysis, Exponential Smoothing, ARIMA
Skills: Time Series Forecasting, Data Cleaning, Moving Average Models

11. Image Classification (CIFAR-10 or MNIST)

Objective: Classify images (such as handwritten digits or CIFAR-10 objects) into different categories.
Key Techniques: Convolutional Neural Networks (CNN), Deep Learning
Skills: TensorFlow, Keras, Image Preprocessing, Model Evaluation

12. Predicting Wine Quality

Objective: Predict the quality of wine based on physicochemical properties.
Key Techniques: Regression, SVM, Random Forest
Skills: Feature Engineering, Model Selection, Hyperparameter Tuning

13. Churn Prediction for Telecom Industry

Objective: Predict if a customer is likely to churn (leave the service) based on customer behavior.
Key Techniques: Classification, Decision Trees, Random Forest
Skills: Data Exploration, Model Evaluation, Data Preprocessing

14. Traffic Sign Classification

Objective: Build a model to classify traffic signs using image data.
Key Techniques: Deep Learning, CNNs
Skills: Image Processing, Data Augmentation, Transfer Learning

15. Heart Disease Prediction

Objective: Predict the presence of heart disease based on patient data.
Key Techniques: Logistic Regression, Decision Trees, Random Forest
Skills: Data Preprocessing, ROC-AUC Analysis, Model Evaluation

16. Titanic Survival Prediction

Objective: Predict the survival of passengers on the Titanic using demographic and other information.
Key Techniques: Logistic Regression, Decision Trees, Random Forest
Skills: Feature Engineering, Model Evaluation, Cross-Validation

17. Predicting Bike Rental Demand

Objective: Predict the demand for bike rentals using the Bike Sharing dataset.
Key Techniques: Regression, Time Series, Random Forest
Skills: Feature Engineering, Model Evaluation, Data Cleaning

18. Loan Approval Prediction

Objective: Predict if a loan application will be approved based on applicant information.
Key Techniques: Classification, Logistic Regression, SVM
Skills: Data Preprocessing, Model Evaluation, Feature Selection

19. Handwritten Digit Recognition

Objective: Classify handwritten digits (using the MNIST dataset).
Key Techniques: Convolutional Neural Networks (CNN), KNN
Skills: Image Processing, Model Training, Hyperparameter Tuning

20. Air Quality Prediction

Objective: Predict air quality index using historical weather and air pollution data.
Key Techniques: Regression, Time Series Forecasting
Skills: Data Visualization, Regression Techniques, Feature Engineering

1. Intermediate Level Projects:

• Credit Card Fraud Detection: Use anomaly detection techniques or classification algorithms to identify fraudulent transactions using credit card data.

• Movie Recommendation System: Create a recommendation system that suggests movies based on user preferences using the MovieLens dataset.

• Customer Segmentation Using Clustering: Perform customer segmentation using clustering algorithms like K-Means to categorize customers based on purchasing behavior.

• Churn Prediction for Telecom Customers: Predict customer churn (when customers stop using a service) using a classification model based on telecom data.

• Sentiment Analysis on Social Media: Build a model to analyze sentiment (positive, negative, neutral) in tweets or reviews using Natural Language Processing (NLP).

• Human Activity Recognition: Use accelerometer data to classify different human activities (walking, running, sitting, etc.) using datasets like the UCI HAR dataset.

2. Advanced Level Projects:

• Real-Time Object Detection: Build an object detection model using deep learning frameworks (like TensorFlow or PyTorch) to detect objects in images or video streams (e.g., YOLO, SSD models).

• Time Series Forecasting (Sales or Energy Consumption): Develop a time series forecasting model to predict future sales, weather, or energy consumption using methods like ARIMA, Prophet, or LSTM networks.

• Natural Language Processing (NLP) for Text Summarization: Use NLP techniques like transformers (BERT, GPT-3) to create a model that can automatically summarize long documents.

• Handwritten Digit Recognition (MNIST): Implement a deep learning model to recognize handwritten digits from the MNIST dataset using Convolutional Neural Networks (CNNs).

• Image Classification with Transfer Learning: Train an image classification model using transfer learning techniques with pre-trained models (e.g., ResNet, VGG, Inception) on a dataset of your choice.

• Autonomous Driving Simulation: Create a self-driving car simulation using reinforcement learning to navigate a vehicle in a virtual environment.

Tools and Libraries You Might Need:

• Libraries: scikit-learn, pandas, matplotlib, seaborn, TensorFlow, Keras, XGBoost

• Datasets: Available from sources like Kaggle, UCI Machine Learning Repository, or OpenML.