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Beginner ML Projects

The best way to learn Machine Learning is by building. These three projects are the "Hello World" of ML, covering the fundamental types of supervised and unsupervised learning.

Project 1: House Price Predictor (Regression)​

Goal: Predict the continuous price of a house based on features like square footage, number of bedrooms, and location.

Project Overview​

This project introduces Linear Regression. You will learn how to handle numerical data and minimize the error between your prediction and the actual price.

  • Dataset: Ames Housing Dataset or California Housing.
  • Key Algorithm: LinearRegression or RandomForestRegressor.
  • Primary Metric: Mean Squared Error (MSE) or R2R^2 Score.

Implementation Steps​

  1. Exploratory Data Analysis (EDA): Visualize correlations using a heatmap.
  2. Preprocessing: Handle missing values and scale features using StandardScaler.
  3. Training: Split data into 80% training and 20% testing.
  4. Evaluation: Calculate the R2R^2 score to see how much variance your model explains.

Project 2: Iris Flower Classifier (Classification)​

Goal: Predict the species of an iris flower (Setosa, Versicolour, or Virginica) based on its petal and sepal measurements.

Project Overview​

This is the classic "classification" problem. You will learn how to handle categorical targets and evaluate accuracy across multiple classes.

  • Dataset: Iris Dataset (built into Scikit-Learn).
  • Key Algorithm: LogisticRegression or K-Nearest Neighbors (KNN).
  • Primary Metric: Accuracy and the Confusion Matrix.

Implementation Steps​

  1. Pairplots: Use Seaborn to see how the species cluster based on petal width vs length.
  2. Training: Use a Simple Decision Tree to see how the model "splits" the data.
  3. Evaluation: Generate a classification report to check Precision and Recall for each flower type.

Project 3: Customer Segmentation (Clustering)​

Goal: Group customers into "segments" based on their spending habits and income without using any pre-defined labels.

Project Overview​

This project introduces Unsupervised Learning. Unlike the first two, there is no "correct answer." You are asking the model to find hidden patterns.

Implementation Steps​

  1. Feature Selection: Focus on "Annual Income" and "Spending Score."
  2. The Elbow Method: Run K-Means for k=1k=1 to 1010 to find the optimal number of clusters.
  3. Visualization: Plot the clusters in different colors and identify the "Big Spenders" vs "Frugal" groups.

Project Workflow Summary​

The following diagram illustrates the standard workflow you should follow for every beginner project.

  • Google Colab: No setup required; run Python in your browser.
  • Scikit-Learn: The industry-standard library for classical ML.
  • Pandas & NumPy: For data manipulation.
  • Matplotlib & Seaborn: For data visualization.

References​

Building these projects provides the foundation for more complex systems. Once you have mastered these, are you ready to tackle real-world case studies?