Recognized by Google, Microsoft, and IBM 🧑‍🎓 • Building Scalable AI Agents 🧠• Git/GitHub • LLM • Agents • AI • GenAI

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Samarth Pujari  • Peerlist

✅ Development Environment🔹Anaconda 🐍 – Package management &amp; environment setup 🔹Jupyter Notebook 📓 – Interactive coding &amp; model development ✅ Programming &amp; Frameworks 🔹Python 🐍 – Core language for model development 🔹Streamlit 📊 – Web-based deployment for interactive UI ✅ Machine Learning &amp; Data Processing 🔹Scikit-Learn 🤖 – Model training &amp; evaluation 🔹Pandas &amp; NumPy 📊 – Data manipulation &amp; preprocessing 🔹Matplotlib &amp; Seaborn 📈 – Data visualization ✅ Model Development &amp; Deployment 🔹Crop Recommendation Model 🌾 – AI-driven crop selection 🔹Fertilizer Recommendation Model 🧪 – Optimal fertilizer guidance 🔹Streamlit Cloud ☁️ – Web app hosting &amp; deployment 📌 Data Collection &amp; Preprocessing 🔹Used two datasets (Crop &amp; Fertilizer) with relevant features 🔹Performed Exploratory Data Analysis (EDA) for insights 🔹Applied Label Encoding for categorical variables 🔹Split datasets into training &amp; testing sets using Scikit-Learn 📌Model Selection &amp; Training 🔹Tested multiple Machine Learning models: - Logistic Regression, GaussianNB, SVC, KNN, DecisionTree, ExtraTree, RandomForest, Bagging, Gradient Boosting, AdaBoost, CatBoost, LGBM 🔹Compared model performance using accuracy &amp; validation metrics 📌Ensemble Learning for Optimization 🔹Evaluated various ensemble techniques: - Voting Classifier, Stacking, Averaging Probabilities, Weighted Ensemble, Blend Ensemble (Custom Blending) 🔹Blend Ensemble provided the best results &amp; was selected 🔹Cross-validated the final model for robustness 📌Deployment &amp; Integration 🔹Exported models as .pkl files (crop_recommendation.pkl, fertilizer_recommendation.pkl) 🔹Integrated models into a Streamlit (.py) app for real-time recommendations🔹Deployed the application on Streamlit Cloud 🔹Integrated real-time insights into the application: - Model Performance Metrics (Accuracy Tracking) 📊 - Feature Importance Analysis 🔍 - Feature Distributions 📉 - Prediction Probabilities 🎯