How to Build Your First Machine Learning Model from Scratch

 🧠 Project: Build a Machine Learning Model to Predict Iris Flower Species

We’ll use the classic Iris dataset to train a model that predicts the type of iris flower based on features like petal length and width.

✅ Step-by-Step Guide

Step 1: Install Required Libraries

If you don’t already have them installed:

pip install scikit-learn pandas matplotlib

Step 2: Import Libraries

import pandas as pd

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score

Step 3: Load the Dataset

iris = load_iris()

df = pd.DataFrame(iris.data, columns=iris.feature_names)

df['species'] = iris.target

Step 4: Explore the Data (Optional but Important)

print(df.head())          # Preview first few rows

print(df['species'].value_counts())  # Check class distribution

Step 5: Prepare the Data

Split the dataset into features (X) and labels (y):

X = df[iris.feature_names]  # Features

y = df['species']           # Labels

Now split into training and testing data:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 6: Choose a Model and Train It

We’ll use a Random Forest Classifier — it’s simple and works well for beginners.

model = RandomForestClassifier()

model.fit(X_train, y_train)

Step 7: Make Predictions

y_pred = model.predict(X_test)

Step 8: Evaluate the Model

accuracy = accuracy_score(y_test, y_pred)

print("Model Accuracy:", accuracy)

You should see something like Model Accuracy: 0.96 — that means 96% accuracy on the test data!

Step 9: Try a Custom Prediction

sample = [[5.1, 3.5, 1.4, 0.2]]  # Input feature values

prediction = model.predict(sample)

print("Predicted Species:", iris.target_names[prediction[0]])

🏁 Summary

You just built your first machine learning model from scratch using:

A real-world dataset

A training/test split

A classification algorithm

Accuracy evaluation

A prediction example

🚀 What's Next?

Try other algorithms like KNeighborsClassifier or SVC.

Visualize the data using matplotlib or seaborn.

Try using your own dataset (e.g., CSV file).

Upload this project to GitHub or Colab.

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