5 Real-World Machine Learning Projects You Can Build This Weekend

 5 Real-World Machine Learning Projects You Can Build This Weekend

Building machine learning projects using real-world datasets is one of the most effective ways to apply your skills. Working with these datasets allows you to deal with challenges like cleaning messy data and handling class imbalance. To build truly useful machine learning models, it's essential to go beyond just training and evaluating models and create APIs and dashboards when necessary.

In this guide, we’ll outline five machine learning projects you can complete over a weekend—using publicly available datasets. For each project, we provide:

• The dataset to use
• The goal of the project
• Areas of focus for learning key concepts
• Tasks to prioritize when building the model

Let’s dive in!

1. House Price Prediction Using the Ames Housing Dataset

This project is a great starting point, focusing on regression. It involves predicting house prices based on various input features.

Goal: Build a regression model to predict house prices.

Dataset: Ames Housing Dataset

Areas of Focus:

• Linear regression
• Feature engineering and selection
• Regression model evaluation

Key Tasks:

• Perform exploratory data analysis (EDA) to understand the dataset.
• Impute missing values and handle categorical features.
• Apply feature engineering to numerical columns.
• Evaluate the model using RMSE (Root Mean Squared Error).

Consider deploying the model via Flask or FastAPI to create an API where users can input house details and receive a price prediction.

 

2. Sentiment Analysis of Tweets

Sentiment analysis is widely used to monitor customer feedback. In this project, you will classify tweets as positive, negative, or neutral.

Goal: Build a sentiment analysis model for tweet classification.

Dataset: Twitter Sentiment Analysis Dataset

Areas of Focus:

• Natural Language Processing (NLP)
• Text preprocessing
• Text classification

Key Tasks:

• Preprocess text data.
• Use TF-IDF or word embeddings to convert text into numerical features.
• Train a classification model and evaluate its performance.

For further experience, build an API where users can input tweets and receive real-time sentiment analysis.

 

3. Customer Segmentation Using the Online Retail Dataset

Customer segmentation is vital for businesses aiming to improve marketing strategies. In this project, you will segment customers based on their purchasing behavior.

Goal: Segment customers into distinct groups based on their purchasing patterns.

Dataset: OnlineRetail Dataset

Areas of Focus:

• Clustering techniques (K-Means, DBSCAN)
• Feature engineering
• RFM analysis (Recency, Frequency, Monetary)

Key Tasks:

• Preprocess the dataset.
• Calculate RFM scores.
• Apply K-Means or DBSCAN to create customer segments.
• Evaluate using the silhouette score.

You can also create an interactive dashboard using Streamlit or Plotly Dash to visualize customer segments and key metrics such as Customer Lifetime Value (CLV).

 

4. Customer Churn Prediction Using the Telco Customer Churn Dataset

Customer churn prediction helps businesses retain subscribers. In this project, you’ll build a model that predicts whether a customer is likely to leave based on various factors.

Goal: Predict customer churn.

Dataset: Telco Customer Churn Dataset

Areas of Focus:

• Classification techniques
• Class imbalance handling
• Feature engineering

Key Tasks:

• Perform EDA and preprocess the data.
• Handle class imbalance.
• Train and evaluate a classification model.

You can also build a dashboard to analyze churn risk factors and display predictions.

 

5. Movie Recommendation System Using the MovieLens Dataset

Recommendation systems are widely used in industries like e-commerce and streaming. In this project, you’ll build a system that suggests movies based on user preferences.

Goal: Build a recommendation system.

Dataset: MovieLens Dataset

Areas of Focus:

• Collaborative filtering
• Matrix factorization (SVD)
• Content-based filtering

Key Tasks:

• Preprocess the data.
• Implement user-item collaborative filtering and matrix factorization.
• Explore content-based filtering.

Deploy the recommendation system to a cloud platform with an API, allowing users to input their movie preferences and receive personalized recommendations.

 

Wrapping Up

Working on these machine learning projects will allow you to apply your skills to solve real-world problems. Go beyond just building models in Jupyter notebooks by creating APIs and dashboards for a complete, practical experience.

What are you waiting for? Grab some coffee and start coding!