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When you read through topics in a Machine Learning book, it's very easy to fall into a trap of thinking that you got it. However, when you implement that simple algorithm yourself, you will often find that you can't. Some really interesting machine learning projects for beginners are listed below:

1. Predicting Iris Flower Species:

   • Use the famous Iris dataset to build a classification model that predicts the species of iris flowers based on features like petal length and width.
2. House Price Prediction:
   • Work with a dataset of housing prices and create a regression model to predict the prices based on features such as square footage, number of bedrooms, and location.
3. Handwritten Digit Recognition:
   • Use the MNIST dataset to build a digit recognition model. This project is great for learning image classification.
4. Sentiment Analysis on Movie Reviews:
   • Create a model that can predict the sentiment (positive or negative) of movie reviews. You can use a dataset like the IMDB movie reviews dataset.
5. Credit Card Fraud Detection:
   • Work with a dataset of credit card transactions and build a model to detect fraudulent transactions. This is a good introduction to working with imbalanced datasets.
6. Breast Cancer Classification:
   • Use a dataset like the Breast Cancer Wisconsin (Diagnostic) dataset to build a binary classification model that predicts whether a tumor is malignant or benign.
7. Spam Email Classifier:
   • Build a model to classify emails as spam or non-spam (ham). Use a dataset of labeled emails for training.
8. Predicting Employee Attrition:
   • Use HR data to predict employee attrition. Create a model that predicts whether an employee is likely to leave the company based on features like job satisfaction, salary, and work-life balance.
9. Predicting Diabetes Onset:
   • Use a diabetes dataset to build a model that predicts the onset of diabetes based on features like glucose levels, BMI, and blood pressure.
10. Image Recognition with CIFAR-10:
    • Work with the CIFAR-10 dataset to build an image recognition model. CIFAR-10 consists of 60,000 32x32 color images in 10 different classes.
11. Movie Recommender System:
    • Build a basic movie recommender system using collaborative filtering or content-based filtering. You can use a movie dataset with user ratings.
12. Twitter Sentiment Analysis:
• nalyze the sentiment of tweets using natural language processing. You can use the Twitter API to collect tweets and classify them as positive, negative, or neutral.

13. Ridge regression.
14. Decision tree:
    • Random forest is a good try.
15. k nearest neighbors:
    • k nearest neighbors with a few different distance measures (Euclidean and L1 distance).
16. Quadratic discriminant analysis:
    • Try to regularize the covariance matrix for improving classification accuracy when the input data is high dimensional (d > 30).
17. Multinomial naive Bayes:
    • Multinomial naive Bayes for categorical data.
18. Linear Support-Vector Machines:
    • This is advanced try without a tutorial.

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