Building Your First Machine Learning Model in Python

Machine learning (ML) is revolutionizing industries by enabling computers to learn from data and make predictions. If you're new to ML, Python is the perfect language to start with due to its simplicity and powerful libraries. In this guide, you'll learn how to build your first machine learning model using Python.

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Step 1: Install Necessary Libraries

Before building a machine learning model, ensure you have the required Python libraries installed. You can install them using:

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pip install numpy pandas scikit-learn matplotlib seaborn

These libraries help with data handling, model training, and visualization.

Step 2: Import Essential Libraries

Start by importing the necessary Python libraries:

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import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_absolute_error, mean_squared_error

Step 3: Load and Explore the Dataset

For this tutorial, we’ll use a sample dataset from scikit-learn:

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from sklearn.datasets import load_boston

boston = load_boston()

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

df['PRICE'] = boston.target

Check the first few rows of the dataset:

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print(df.head())

Step 4: Preprocess the Data

Splitting the dataset into training and testing sets:

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X = df.drop(columns=['PRICE'])

y = df['PRICE'] X_train,

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

Step 5: Train a Machine Learning Model

We'll use Linear Regression, a fundamental supervised learning algorithm:

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model = LinearRegression()

model.fit(X_train, y_train)

Step 6: Make Predictions

After training, test the model on new data:

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y_pred = model.predict(X_test)

Step 7: Evaluate the Model

Assess model performance using common metrics:

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mae = mean_absolute_error(y_test, y_pred)

mse = mean_squared_error(y_test, y_pred)

print(f"Mean Absolute Error: {mae}")

print(f"Mean Squared Error: {mse}")

Step 8: Visualize Results

Plot actual vs. predicted prices:

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plt.scatter(y_test, y_pred)

plt.xlabel("Actual Prices")

plt.ylabel("Predicted Prices")

plt.title("Actual vs. Predicted Prices")

plt.show()

Conclusion

Congratulations! You've built your first machine learning model using Python. By following these steps, you have learned how to:

• Load and explore a dataset
• Preprocess data
• Train a machine learning model
• Evaluate performance

As you advance, explore more algorithms like Decision Trees, Random Forests, and Neural Networks to enhance your models.

If you have any python related queries please feel free to reach out Anitha Rajesh .