This project combines data extraction, predictive modeling, and geospatial mapping to analyze housing trends in Mercer County, New Jersey. It consists of three core components: Census Data Extraction: Gathers U.S. Census data (2012–2022) on median house value, household income, and racial demographics for all census tracts in the county. It accounts for changes in census tract boundaries between 2010 and 2020 by approximating values for newly defined tracts. House Value Prediction: Uses an LSTM model with k-fold cross-validation to forecast median house values through 2025. Multiple feature combinations and sequence lengths are tested to optimize prediction accuracy, with the final model selected based on MSE and MAE scores. Data Mapping: Visualizes historical and predicted housing data using GeoJSON files from the TIGERWeb API. It generates interactive maps showing raw values, changes over time, and percent differences, with customization options to handle outliers and improve interpretability. This modular workflow can be adapted to other regions by changing the input FIPS codes and feature selections.

📝 Description: This synthetic dataset is designed to help beginners and intermediate learners practice data cleaning and analysis in a realistic setting. It simulates a student tracking system, covering key areas like:

Attendance tracking 📅

Homework completion 📝

Exam performance 🎯

Parent-teacher communication 📢

✅ Why Use This Dataset? While many datasets are pre-cleaned, real-world data is often messy. This dataset includes intentional errors to help you develop essential data cleaning skills before diving into analysis. It’s perfect for building confidence in handling raw data!

🛠️ Cleaning Challenges You’ll Tackle This dataset is packed with real-world issues, including:

Messy data: Names in lowercase, typos in attendance status.

Inconsistent date formats: Mix of MM/DD/YYYY and YYYY-MM-DD.

Incorrect values: Homework completion rates in mixed formats (e.g., 80% and 90).

Missing data: Guardian signatures, teacher comments, and emergency contacts.

Outliers: Exam scores over 100 and negative homework completion rates.

🚀 Your Task: Clean, structure, and analyze this dataset using Python or SQL to uncover meaningful insights!

📌 5. Handle Outliers

Remove exam scores above 100.

Convert homework completion rates to consistent percentages.

📌 6. Generate Insights & Visualizations

What’s the average attendance rate per grade?

Which subjects have the highest performance?

What are the most common topics in parent-teacher communication?

This resource contains a Python script used to clean and preprocess the alum dosage dataset from a small Oklahoma water treatment plant. The script handles missing values, removes outliers, merges historical water quality and weather data, and prepares the dataset for AI model training.

AQQAD, ABDELRAHIM (2023), “insurance_claims ”, Mendeley Data, V2, doi: 10.17632/992mh7dk9y.2

https://data.mendeley.com/datasets/992mh7dk9y/2

Latest version Version 2 Published: 22 Aug 2023 DOI: 10.17632/992mh7dk9y.2

Data Acquisition: - Obtain the dataset titled "Insurance_claims" from the following Mendeley repository: https://https://data.mendeley.com/drafts/992mh7dk9y - Download and store the dataset locally for easy access during subsequent steps.

Data Loading & Initial Exploration: - Use Python's Pandas library to load the dataset into a DataFrame. python Code used:

Load the Dataset File

insurance_df = pd.read_csv('insurance_claims.csv')

• Inspect the initial rows, data types, and summary statistics to get an understanding of the dataset's structure.

Data Cleaning & Pre-processing: - Handle missing values, if any. Strategies may include imputation or deletion based on the nature of the missing data. - Identify and handle outliers. In this research, particularly, outliers in the 'umbrella_limit' column were addressed. - Normalize or standardize features if necessary.

Exploratory Data Analysis (EDA): - Utilize visualization libraries such as Matplotlib and Seaborn in Python for graphical exploration. - Examine distributions, correlations, and patterns in the data, especially between features and the target variable 'fraud_reported'. - Identify features that exhibit distinct patterns for fraudulent and non-fraudulent claims.

Feature Engineering & Selection: - Create or transform existing features to improve model performance. - Use techniques like Recursive Feature Elimination (RFECV) to identify and retain only the most informative features.

Modeling: - Split the dataset into training and test sets to ensure the model's generalizability. - Implement machine learning algorithms such as Support Vector Machine, RandomForest, and Voting Classifier using libraries like Scikit-learn. - Handle class imbalance issues using methods like Synthetic Minority Over-sampling Technique (SMOTE).

Model Evaluation: - Evaluate the performance of each model using metrics like precision, recall, F1-score, ROC-AUC score, and confusion matrix. - Fine-tune the models based on the results. Hyperparameter tuning can be performed using techniques like Grid Search or Random Search.

Model Interpretation: - Use methods like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to interpret and understand the predictions made by the model.

Deployment & Prediction: - Utilize the best-performing model to make predictions on unseen data. - If the intention is to deploy the model in a real-world scenario, convert the trained model into a format suitable for deployment (e.g., using libraries like joblib or pickle).

Software & Tools: - Programming Language: Python (version: GoogleColab) - Libraries: Pandas, Numpy, Matplotlib, Seaborn, Scikit-learn, Imbalanced-learn, LIME, and SHAP. - Environment: Jupyter Notebook or any Python IDE.

This dataset provides a step-by-step pipeline for preprocessing metabolomics data.

The pipeline implements Probabilistic Quotient Normalization (PQN) to correct dilution effects in metabolomics measurements.

Includes guidance on handling raw metabolomics datasets obtained from LC-MS or NMR experiments.

Demonstrates Principal Component Analysis (PCA) for dimensionality reduction and exploratory data analysis.

Includes data visualization techniques to interpret PCA results effectively.

Suitable for metabolomics researchers and data scientists working on omics data.

Enables better reproducibility of preprocessing workflows for metabolomics studies.

Can be used to normalize data, detect outliers, and identify major patterns in metabolomics datasets.

Provides a Python-based notebook that is easy to adapt to new datasets.

Includes example datasets and code snippets for immediate application.

Helps users understand the impact of normalization on downstream statistical analyses.

Supports integration with other metabolomics pipelines or machine learning workflows.

Socio-demographic and economic characteristics of respondents.

⚡ INDIA ELECTRICITY & ENERGY ANALYSIS PROJECT

This repository presents an extensive data engineering, cleaning, and analytical study on India’s electricity ecosystem using Python. The project covers coal stock status, thermal power generation, renewable energy trends, energy requirements & availability, and installed capacity across states.

The goal is to identify operational bottlenecks, resource deficits, energy trends, and support data-driven decisions in the power sector.

📊 Electricity Data Insights & System Analysis

The project leverages five government datasets:

🔹 Daily Coal Stock Data

🔹 Daily Power Generation

🔹 Renewable Energy Production

🔹 State-wise Energy Requirement vs Availability

🔹 Installed Capacity Across Fuel Types

The final analysis includes EDA, heatmaps, trend analysis, outlier detection, data-cleaning automation, and visual summaries.

🔹 Key Features ✅ 1. Comprehensive Data Cleaning Pipeline

Null value treatment using median/mode strategies

Standardizing categorical inconsistencies

Filling missing regions, states, and production values

Date format standardization

Removing duplicates across all datasets

Large-scale outlier detection using custom 5×IQR logic (to preserve real-world operational variance)

✅ 2. Exploratory Data Analysis (EDA)

Includes:

Coal stock trends over years

Daily power generation patterns

Solar, wind, and renewable growth

State-wise energy shortage & surplus

Installed capacity distribution across India

Correlation maps for all major datasets

✅ 3. Trend Visualizations

📈 Coal Stock Time-Series

🔥 Thermal Power Daily Output

🌞 Solar & Wind Contribution Over Time

🇮🇳 State-wise Energy Deficit Bar Chart

🗺️ MOM Energy Requirement Heatmap

⚙️ Installed Capacity Share of Each State

📌 Dashboard & Analysis Components Section Description 🔹 Coal Stock Dashboard Daily stock, consumption, transport mode, critical plants 🔹 Power Generation Capacity, planned vs actual generation 🔹 Renewable Mix Solar, wind, hydro & total RE contributions 🔹 Energy Shortfall Requirement vs availability across states 🔹 Installed Capacity Coal, Gas, Hydro, Nuclear & RES capacity stacks 🧠 Insights & Findings 🔥 Coal Stock

Critical coal stock days observed for multiple stations

Seasonal dips in stock days & indigenous supply shocks

Import dependency minimal but volatile

⚡ Power Generation

Thermal stations show fluctuating PLF (Plant Load Factor)

Many states underperform planned generation

🌞 Renewable Energy

Solar shows continuous year-over-year growth

Wind output peaks around monsoon months

🔌 Energy Requirement vs Availability

States like Delhi, Bihar, Jharkhand show intermittent deficits

MOM heatmap highlights major seasonal spikes

⚙️ Installed Capacity

Southern & Western regions dominate national capacity

Coal remains the largest but renewable share rising rapidly

📁 Files in This Repository File Description coal_stock.csv Cleaned coal stock dataset power_gen.csv Daily power generation data renewable_engy.csv State-wise renewable energy dataset engy_reqmt.csv Monthly requirement & availability dataset install_cpty.csv Installed capacity across fuel types electricity.ipynb Full Python EDA notebook electricity.pdf Export of full Colab notebook (code + visuals) README.md GitHub project summary

🛠️ Technologies Used 📊 Data Analysis

Python (Pandas, NumPy, Matplotlib, Seaborn)

🧹 Data Cleaning

Null Imputation

Outlier Detection (5×IQR)

Standardization & Encoding

Handling Large Multi-year Datasets

🔧 System Concepts

Modular Python Code

Data Pipelines & Feature Engineering

Version Control (Git/GitHub)

Cloud Concepts (Google Colab + Drive Integration)

📈 Core Metrics & KPIs

Total Stock Days

PLF% (Plant Load Factor)

Renewable Energy Contribution

Energy Deficit (%)

National Installed Capacity Share

📚 Future Enhancements

Build a Power BI dashboard for visual storytelling

Integrate forecasting models (ARIMA / Prophet)

Automate coal shortage alerts

Add state-level energy prediction for seasonality

Deploy the analysis as a web dashboard (Streamlit)