1. Predicting Titanic Survival
A beginner-friendly project using the Titanic dataset to predict if a passenger survived, based on features like age, gender, and class. Focuses on data cleaning, handling missing values, and applying logistic regression or decision trees.
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2. Predicting Stock Prices
A time-series project forecasting stock values using models like ARIMA or LSTM. Includes feature engineering (moving averages, lags) and evaluation with MSE. Data can be sourced from Yahoo Finance.
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3. Building an Email Spam Classifier
A text classification project that detects spam emails using NLP techniques (tokenization, stemming, TF-IDF). Models like Naive Bayes or SVM are often used.
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4. Recognizing Handwritten Digits
Using the MNIST dataset to classify digits (0–9) with CNNs. Great introduction to image preprocessing, convolutional layers, and model evaluation.
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5. Building a Movie Recommendation System
Recommends movies using collaborative filtering or content-based methods. Often applies matrix factorization techniques like SVD on MovieLens data.
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6. Predicting Customer Churn
Predicts which customers are likely to cancel a service. Covers imbalanced datasets and uses logistic regression, random forests, or gradient boosting.
Sample Repos:
- https://github.com/sharmaroshan/Telco-Customer-Churn-Prediction
- https://github.com/IBM/telco-customer-churn-on-icp4d
7. Detecting Faces in Images
An introduction to computer vision using OpenCV and Haar cascades for face detection. Involves image preprocessing and tuning detection parameters.
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