Individual/Group(2 members max.)
Deadline:18 Jan
Please note:
1.This project phase is to be taken seriously and weekly updates will be taken.Failure to do the assigned task would result in termination without any additional warning.
2.Completion of your project alone won't suffice for your retention status.Additional pre assigned task(DL Checklist for e.g.) by the domain leads will also be assessed.
AI/ML:
Regression Projects
1.Stock Price Prediction
Description: Use historical stock data to predict future prices.
Skills: Implement linear regression and explore advanced techniques like Lasso or Ridge regression.
Dataset: Tesla Stock Data from 2010 to 2020.(Or any other)
2.Boston Housing Price Prediction
Description: Predict house prices based on various features such as size and location.
Skills: Apply multiple linear regression and perform exploratory data analysis.
Dataset: Boston Housing dataset available from the UCI Machine Learning.
3.Medical Cost Prediction
Description: Predict medical costs based on individual characteristics.
Skills: Utilize multiple linear regression and understand the impact of different features on costs.
Dataset: Medical Cost Personal Datasets available on Kaggle.
Classification Projects
1.Titanic Survival Prediction
Description: Predict whether a passenger survived the Titanic disaster based on features like age, gender, and class.
Skills: Data preprocessing, feature selection, and using classification algorithms like Decision Trees or Random Forests.
Dataset: Titanic dataset available on Kaggle.
2.Iris Flower Classification
Description: Classify iris flowers into species based on petal and sepal measurements.
Skills: Implement basic classification techniques using the K-nearest neighbors (KNN) algorithm.
Dataset: Iris dataset from the UCI Machine Learning.
3.Credit Card Fraud Detection
Description: Identify fraudulent transactions using transaction data.
Skills: Anomaly detection techniques and classification algorithms like Logistic Regression or Random Forests.
Dataset: Credit card fraud detection datasets available on Kaggle.
Deep Learning Projects
1.Handwritten Digit Recognition (MNIST)
Description: Build a Convolutional Neural Network (CNN) to classify handwritten digits from images.
Skills: Understand CNN architecture and image processing techniques.
Dataset: MNIST dataset available online.
2.Image Classification with CIFAR-10
Description: Classify images into 10 different categories using a deep learning model.
Skills: Implement CNNs and explore data augmentation techniques to improve model performance.
Dataset: CIFAR-10 dataset available from various sources.
3.Sentiment Analysis on Movie Reviews
Description: Use a recurrent neural network (RNN) to analyze sentiments from movie reviews (positive or negative).
Skills: Work with NLP techniques and RNN architectures like LSTM or GRU.
Dataset: IMDB movie reviews dataset.
Other than the aforementioned ideas domain members are free to come up with their own projects which they had been doing or want to do in future.
WEB DEV:
1.Personal Portfolio
Description: Make your own portfolio without any copy paste must have a good ui, it has to be a complete portfolio website
2.Blog post website
Description: Blog posting website with a backend
3.E-com
Description: E-com website that will contain everything that an e-com site need to have (example : wishlist, payment gateway etc)
Requirements : Must be hosted (on github pages or vercel), have a good ui, responsive, js to be used (preffered React), no pull stack dev will be accepted (no copy pasting of code or youtube) Note this is not a grp work and the deadline is same as mentioned above.