🤖 AI with Python – Script Series

Welcome to the official repository for the “AI with Python” series – a practical and beginner-friendly collection of Python scripts shared weekly on Instagram.

📌 What Is This?

This repo is designed to help you:

Learn core concepts of AI, machine learning, and data preprocessing
Write clean, understandable Python code
Apply what you learn in real-world problems

Each script is written with detailed comments, made to be run in Jupyter Notebook, and aligned with a specific AI/ML concept.

🚀 What's Inside?

This repository contains a curated set of beginner-to-intermediate level Python scripts designed to teach core concepts in:

✅ Data preprocessing (scaling, encoding, handling missing values)
✅ Building machine learning models (regression, classification, clustering)
✅ Evaluating and visualizing model performance
✅ Creating end-to-end ML pipelines
✅ Real-world use cases like sentiment analysis, spam detection, and customer segmentation

📚 How To Use?

Clone this repo or download individual notebooks
Open in Jupyter Notebook / JupyterLab
Run each cell and follow the comments
Check the corresponding Instagram post for visuals & tips

✨ Follow Along

I post 2 new scripts every week on Instagram as part of the "AI with Python" series.
📲 Follow me to keep learning, one script at a time.

🛠️ Tech Stack

Python 3.8+
Jupyter Notebook
scikit-learn
pandas
matplotlib / seaborn (where needed)

📩 License

This project is open-source under the MIT License.

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🤖 AI with Python – Script Series

📌 What Is This?

🚀 What's Inside?

📚 How To Use?

✨ Follow Along

🛠️ Tech Stack

📩 License

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Name		Name	Last commit message	Last commit date
Latest commit History 76 Commits
.ipynb_checkpoints		.ipynb_checkpoints
datasets		datasets
models		models
python_files		python_files
.gitignore		.gitignore
Build a FastAPI Prediction API.ipynb		Build a FastAPI Prediction API.ipynb
Classify Movie Reviews as Positive or Negative.ipynb		Classify Movie Reviews as Positive or Negative.ipynb
Cluster customers using KMeans.ipynb		Cluster customers using KMeans.ipynb
Correlation Matrix with Annotations using Seaborn.ipynb		Correlation Matrix with Annotations using Seaborn.ipynb
Credit Card Fraud Detection (Imbalanced ML).ipynb		Credit Card Fraud Detection (Imbalanced ML).ipynb
Cross-validation with cross_val_score.ipynb		Cross-validation with cross_val_score.ipynb
Customer Churn Prediction (RandomForest + SMOTE).ipynb		Customer Churn Prediction (RandomForest + SMOTE).ipynb
Customer Segmentation using KMeans.ipynb		Customer Segmentation using KMeans.ipynb
Deploy FastAPI ML App on Render_Railway.ipynb		Deploy FastAPI ML App on Render_Railway.ipynb
Dockerizing a Machine Learning Model.ipynb		Dockerizing a Machine Learning Model.ipynb
Encode Categorical Data using LabelEncoder.ipynb		Encode Categorical Data using LabelEncoder.ipynb
End-to-end pipeline using Pipeline.ipynb		End-to-end pipeline using Pipeline.ipynb
Evaluate a Classifier using Confusion Matrix & Accuracy Score.ipynb		Evaluate a Classifier using Confusion Matrix & Accuracy Score.ipynb
Export Trained Model using joblib.ipynb		Export Trained Model using joblib.ipynb
Feature Interaction Heatmaps.ipynb		Feature Interaction Heatmaps.ipynb
Feature Selection using SelectKBest.ipynb		Feature Selection using SelectKBest.ipynb
Feature scaling impact on KNN accuracy.ipynb		Feature scaling impact on KNN accuracy.ipynb
Find optimal clusters for K-Means using Elbow Method.ipynb		Find optimal clusters for K-Means using Elbow Method.ipynb
Fit ML Model using LinearRegression.ipynb		Fit ML Model using LinearRegression.ipynb
Gradio ML Demo Interface.ipynb		Gradio ML Demo Interface.ipynb
Grid Search using GridSearchCV.ipynb		Grid Search using GridSearchCV.ipynb
Handle Missing Data with SimpleImputer.ipynb		Handle Missing Data with SimpleImputer.ipynb
Heart Disease Prediction (UCI Dataset).ipynb		Heart Disease Prediction (UCI Dataset).ipynb
Image Classifier with KNeighborsClassifier.ipynb		Image Classifier with KNeighborsClassifier.ipynb
Interactive Confusion Matrix with Plotly.ipynb		Interactive Confusion Matrix with Plotly.ipynb
KNN Classifier in 10 lines.ipynb		KNN Classifier in 10 lines.ipynb
Loan Approval Prediction.ipynb		Loan Approval Prediction.ipynb
Logging & Monitoring Model Inputs_Outputs.ipynb		Logging & Monitoring Model Inputs_Outputs.ipynb
Model Calibration Curves.ipynb		Model Calibration Curves.ipynb
Model Evaluation Metrics.ipynb		Model Evaluation Metrics.ipynb
Model Optimization & Evaluation.ipynb		Model Optimization & Evaluation.ipynb
Multiclass Classification Evaluation with classification_report.ipynb		Multiclass Classification Evaluation with classification_report.ipynb
Normalise Data using MinMaxScaler.ipynb		Normalise Data using MinMaxScaler.ipynb
Online Sales Demand Forecasting.ipynb		Online Sales Demand Forecasting.ipynb
Pairplot Visualization using seaborn.ipynb		Pairplot Visualization using seaborn.ipynb
Partial Dependence Plots (PDP).ipynb		Partial Dependence Plots (PDP).ipynb
Permutation Feature Importance.ipynb		Permutation Feature Importance.ipynb
Plot feature importance from Decision Tree.ipynb		Plot feature importance from Decision Tree.ipynb
Polynomial Regression with scikit-learn.ipynb		Polynomial Regression with scikit-learn.ipynb
Predict Diabetes Progression.ipynb		Predict Diabetes Progression.ipynb
Predict House Prices using scikit-learn.ipynb		Predict House Prices using scikit-learn.ipynb
Predict heart disease from UCI Dataset.ipynb		Predict heart disease from UCI Dataset.ipynb
Predict wine quality using RandomForestClassifier.ipynb		Predict wine quality using RandomForestClassifier.ipynb
Predictions vs Actuals Over Time.ipynb		Predictions vs Actuals Over Time.ipynb
README.md		README.md
ROC Curve using sklearn.metrics.ipynb		ROC Curve using sklearn.metrics.ipynb
RandomForestClassifier for Tabular Data.ipynb		RandomForestClassifier for Tabular Data.ipynb
Regularization in Linear Models – Ridge vs Lasso.ipynb		Regularization in Linear Models – Ridge vs Lasso.ipynb
Reliability Diagrams.ipynb		Reliability Diagrams.ipynb
Running ML Inference Inside Docker.ipynb		Running ML Inference Inside Docker.ipynb
SHAP Values for Model Explanation.ipynb		SHAP Values for Model Explanation.ipynb
SHAP values for model interpretability.ipynb		SHAP values for model interpretability.ipynb
Save & Load ML Models using joblib.ipynb		Save & Load ML Models using joblib.ipynb
Sentiment Analysis with TextBlob.ipynb		Sentiment Analysis with TextBlob.ipynb
Solar Energy Output Prediction.ipynb		Solar Energy Output Prediction.ipynb
Spam detection with CountVectorizer + MultinomialNB.ipynb		Spam detection with CountVectorizer + MultinomialNB.ipynb
Split Data into Training & Testing using train_test_split.ipynb		Split Data into Training & Testing using train_test_split.ipynb
Standardize Data using StandardScaler.ipynb		Standardize Data using StandardScaler.ipynb
Streamlit ML App UI for Predictions.ipynb		Streamlit ML App UI for Predictions.ipynb
Titanic Survival Predictor with Improved Feature Engineering.ipynb		Titanic Survival Predictor with Improved Feature Engineering.ipynb
Train a Decision Tree Classifier on the Iris Dataset.ipynb		Train a Decision Tree Classifier on the Iris Dataset.ipynb
Train a Support Vector Machine (SVM) for classification.ipynb		Train a Support Vector Machine (SVM) for classification.ipynb
Visualize Classification Boundaries with matplotlib.ipynb		Visualize Classification Boundaries with matplotlib.ipynb
Visualize Feature Correlation Heatmap.ipynb		Visualize Feature Correlation Heatmap.ipynb
Visualize digits using matplotlib.ipynb		Visualize digits using matplotlib.ipynb
Visualizing Decision Boundaries for SVM.ipynb		Visualizing Decision Boundaries for SVM.ipynb
Wine Quality Prediction (RandomForest + SHAP).ipynb		Wine Quality Prediction (RandomForest + SHAP).ipynb

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🤖 AI with Python – Script Series

📌 What Is This?

🚀 What's Inside?

📚 How To Use?

✨ Follow Along

🛠️ Tech Stack

📩 License

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