Top 15 Beginner Machine Learning Projects to Boost Your Portfolio
Building real-world projects is the fastest way to solidify your machine learning skills and stand out to employers. Below are 15 beginner-friendly ML project ideas—each comes with a clear goal, suggested dataset, and key learning outcomes to guide your hands-on journey.
1. Iris Flower Classification
Dataset: Fisher’s Iris
Goal: Train a classifier to predict flower species based on sepal/petal measurements.
Skills: Data loading, train/test split, Logistic Regression, evaluation metrics.
2. Housing Price Prediction
Dataset: Boston Housing or Kaggle’s House Prices
Goal: Predict home prices using features like location, size, and age.
Skills: Linear Regression, feature engineering, error metrics (MSE/R²).
3. Handwritten Digit Recognition
Dataset: MNIST
Goal: Build a simple neural network to classify digits 0–9.
Skills: Keras/TensorFlow, dense layers, softmax, model training/validation.
4. Sentiment Analysis on Tweets
Dataset: Kaggle Twitter US Airline Sentiment
Goal: Classify tweets as positive, negative, or neutral.
Skills: Text preprocessing, TF-IDF vectorization, Naive Bayes or logistic regression.
5. Customer Segmentation
Dataset: Mall Customer Segmentation Data
Goal: Use clustering to group customers by spending behavior.
Skills: K-means, PCA for visualization, silhouette score.
6. Spam Email Classifier
Dataset: UCI Spam Base
Goal: Detect spam vs. ham emails.
Skills: Text preprocessing, feature extraction, ensemble methods.
7. Movie Recommendation System
Dataset: MovieLens
Goal: Suggest movies based on user ratings.
Skills: Collaborative filtering, matrix factorization, cosine similarity.
8. Image Colorization
Dataset: CIFAR-10 or COCO subset
Goal: Convert grayscale images to color.
Skills: Convolutional Autoencoders, image preprocessing.
9. Stock Price Forecasting
Dataset: Yahoo Finance historical data
Goal: Predict future stock prices with time-series models.
Skills: ARIMA/LSTM, sliding windows, evaluation on MAE/MAPE.
10. Chest X-Ray Pneumonia Detection
Dataset: NIH Chest X-ray Dataset
Goal: Classify images as pneumonia or healthy.
Skills: Transfer learning with pre-trained CNNs (e.g. ResNet).
11. Real-Time Object Detection
Dataset: COCO or Pascal VOC
Goal: Detect objects in live webcam feed.
Skills: YOLO/SSD models, OpenCV integration.
12. Chatbot with NLP
Dataset: Cornell Movie Dialogs
Goal: Build a simple conversational agent.
Skills: Seq2Seq models, attention mechanism.
13. Music Genre Classification
Dataset: GTZAN
Goal: Classify audio tracks by genre.
Skills: Audio feature extraction (MFCC), Random Forests/SVM.
14. Traffic Sign Recognition
Dataset: German Traffic Sign Dataset
Goal: Identify road signs from images.
Skills: CNNs, data augmentation.
15. Customer Churn Prediction
Dataset: Telco Customer Churn (Kaggle)
Goal: Predict if a customer will leave a service.
Skills: Classification trees, feature importance, ROC analysis.