Welcome to Foundation of Data Science Laboratory
Welcome to Foundation of Data Science Laboratory
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.