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AI/ML Project

Sharpen your AI & Machine Learning skills with these structured projects — starting from beginner-friendly ML models, moving into advanced AI applications, and finally tackling a Mega Project (AI Personal Assistant) .

🟢 Simple Projects​

House Price Prediction​

House Price Prediction

Description:
Predict house prices based on features like size, location, and number of rooms.

How to Build:

  • Collect dataset (e.g., from Kaggle).
  • Perform data cleaning (handle missing values, outliers).
  • Use regression models (Linear Regression, Decision Trees).
  • Evaluate performance using metrics like RMSE or R².

Sentiment Analysis​

Sentiment Analysis

Description:
Classify text (tweets, reviews, comments) as positive, negative, or neutral.

How to Build:

  • Use a labeled dataset (IMDB reviews, Twitter data).
  • Preprocess text (tokenization, stop-word removal, lemmatization).
  • Train a classifier (Logistic Regression, Naive Bayes, or simple Neural Network).
  • Test the model with new sentences.

Handwritten Digit Recognition​

Handwritten Digit Recognition (MNIST)

Description:
Recognize handwritten digits (0–9) from images.

How to Build:

  • Use the MNIST dataset (60k training, 10k test images).
  • Normalize image pixel values.
  • Train a simple Neural Network or CNN (Convolutional Neural Network).
  • Evaluate accuracy on unseen digits.

Movie Recommendation System​

Movie Recommendation System

Description:
Suggest movies to users based on their preferences.

How to Build:

  • Use a dataset like MovieLens.
  • Implement collaborative filtering (user-user or item-item).
  • Optionally, use content-based filtering with genres, tags.
  • Show personalized recommendations for each user.

🔴 Advanced Projects​

Fraud Detection System​

Fraud Detection

Description:
Detect fraudulent transactions in banking/finance datasets.

How to Build:

  • Collect transaction data (imbalanced dataset).
  • Apply feature engineering (transaction amount, location, frequency).
  • Use classification models (Random Forest, Gradient Boosting, Neural Networks).
  • Handle imbalance with oversampling/undersampling or SMOTE.

Image Caption Generator​

Image Caption Generator

Description:
Generate descriptive captions for images automatically.

How to Build:

  • Use an image dataset with captions (COCO dataset).
  • Extract image features using CNN (e.g., ResNet, VGG).
  • Use an LSTM/Transformer model to generate text.
  • Combine into an encoder-decoder architecture.

Chatbot with NLP​

AI Chatbot

Description:
A conversational chatbot that answers user queries.

How to Build:

  • Collect dataset (FAQ-based, or use open-domain datasets).
  • Use NLP techniques for intent recognition.
  • Train with RNN/LSTM or Transformer-based models (BERT, GPT).
  • Integrate with a web interface or messaging app.

Stock Price Prediction​

Stock Price Prediction

Description:
Predict future stock prices using historical data.

How to Build:

  • Collect stock market data (Yahoo Finance, Alpha Vantage).
  • Perform time series analysis.
  • Use models like ARIMA, LSTM, or Transformer models for forecasting.
  • Visualize predictions vs actual data.

🧮 Mega Project​

AI Personal Assistant​

AI Personal Assistant

Description:
Build a Jarvis-like AI assistant capable of voice commands, task automation, and web queries.

How to Build:

  • Integrate speech-to-text (e.g., Google Speech API).
  • Use NLP models for intent detection.
  • Add task modules (send email, play music, fetch news, control smart devices).
  • Implement text-to-speech for natural voice responses.
  • Continuously improve with reinforcement learning or fine-tuned models.