Day 1: Introduction to TOC Data Analysis and Python Basics

• Significance, sources, and impact of Total Organic Carbon in various industries (e.g., environmental monitoring, water treatment)
• Overview of Python libraries (Pandas, NumPy), data manipulation, and preprocessing techniques
• Reading data from CSV, Excel, and JSON formats, handling missing values, and basic data exploration
• Tools: Pandas, NumPy, Matplotlib & Seaborn,Jupyter Notebook
• Mini Task: Load a TOC dataset, clean it, and perform basic descriptive analysis (mean, median, standard deviation)

Day 2: Data Exploration, Visualization, and Feature Engineering

• Visualizing TOC data with Matplotlib, seaborn, and understanding distribution patterns
• Identifying useful features, scaling, and transforming TOC data for machine learning models
• Understanding correlations in TOC data and its relationship with other variables
• Tools: Scikit-learn: Pandas Profiling, Matplotlib & Seaborn
• Mini Task: Perform EDA on a TOC dataset, visualize key trends, and identify relevant features for prediction

Day 3: Machine Learning Models for TOC Prediction

• Overview of supervised learning algorithms (Linear Regression, Decision Trees, Random Forests)
• Splitting data into training and testing sets, evaluating model performance (accuracy, RMSE)
• Using Scikit-learn to build and train a machine learning model on TOC data
• Hyperparameter tuning and model evaluation using cross-validation
• Tools: Scikit-learn, GridSearchCV & RandomizedSearchCV: XGBoost / LightGBM, Matplotlib & Seaborn
• Mini Task: Build a machine learning model to predict TOC values and evaluate its performance

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