10 Machine Learning Projects that beginers can do to hone their skills

Here’s a list of 10 beginner-friendly Machine Learning projects that help you build core skills in data preprocessing, model selection, and evaluation — with increasing complexity:

---

✅ 1. Titanic Survival Prediction

Goal: Predict whether a passenger survived the Titanic disaster based on features like age, class, sex, etc.

• Type: Classification

• Dataset: Kaggle Titanic Dataset

• Skills: Data cleaning, handling missing values, logistic regression, decision trees.

---

✅ 2. House Price Prediction

Goal: Predict the sale price of a house based on features like area, location, number of rooms, etc.

• Type: Regression

• Dataset: Kaggle Housing Prices

• Skills: Feature engineering, linear regression, random forest, evaluation metrics (RMSE).

---

✅ 3. Iris Flower Classification

Goal: Classify iris flowers into three species using sepal and petal dimensions.

• Type: Classification

• Dataset: Inbuilt in scikit-learn

• Skills: Basic classification, data visualization, model accuracy.

---

✅ 4. Handwritten Digit Recognition

Goal: Recognize handwritten digits (0–9) using image data.

• Type: Image Classification

• Dataset: MNIST (available via TensorFlow or sklearn)

• Skills: Image data handling, CNN basics, accuracy evaluation.

---

✅ 5. Movie Recommendation System

Goal: Recommend movies to users based on their ratings.

• Type: Recommendation System

• Dataset: MovieLens Dataset

• Skills: Collaborative filtering, cosine similarity, matrix factorization.

---

✅ 6. Spam Email Classifier

Goal: Classify whether an email is spam or not using text analysis.

• Type: Text Classification

• Dataset: UCI Spam Dataset

• Skills: NLP preprocessing (TF-IDF), Naive Bayes, SVM.

---

✅ 7. Stock Price Prediction (Simple)

Goal: Predict future stock prices based on historical data.

• Type: Time Series Forecasting

• Dataset: Yahoo Finance API or yfinance library

• Skills: Time series visualization, ARIMA, LSTM (advanced).

---

✅ 8. Customer Segmentation

Goal: Group customers into clusters based on purchasing behavior.

• Type: Clustering (Unsupervised Learning)

• Dataset: Mall Customers Dataset

• Skills: K-Means clustering, PCA, elbow method.

---

✅ 9. Fake News Detection

Goal: Predict whether a given news article is real or fake.

• Type: Binary Classification

• Dataset: Fake News Dataset

• Skills: Text vectorization (TF-IDF, CountVectorizer), logistic regression.

---

✅ 10. Heart Disease Prediction

Goal: Predict the presence of heart disease using medical attributes.

• Type: Classification

• Dataset: UCI Heart Disease Dataset

• Skills: Logistic regression, ROC-AUC, model evaluation.

---

🛠 Tips for Each Project:

• Start with Exploratory Data Analysis (EDA).

• Use scikit-learn for models and matplotlib/seaborn for plots.

• Split into training/testing sets using train_test_split().

• Try 2–3 different algorithms and compare.