Weather_Trend_Forecasting

PM Accelerator (Data Scientist)

Weather Trend Forecasting - Basic Analysis

Project Overview

This project focuses on analyzing weather trends using fundamental exploratory data analysis (EDA) techniques and simple forecasting models. The dataset used is GlobalWeatherRepository.csv, containing information on temperature, humidity, and other weather-related attributes. The goal is to clean the data, perform initial analysis, and build a basic predictive model.

Objectives

• Conduct basic EDA to understand weather trends.
• Handle missing values and perform data cleaning.
• Implement a simple forecasting model for temperature prediction.
• Visualize key weather patterns using plots and graphs.
• Store results and visualizations in a structured report.

Dataset

• Source: GlobalWeatherRepository.csv
• Features: Temperature, humidity, wind speed, location, timestamp.
• Target Variable: Temperature (Celsius).

Data Processing

1. Data Cleaning

   • Remove or impute missing values.
   • Convert timestamp columns to datetime format.
   • Standardize column names for consistency.

2. Feature Engineering

   • Extract relevant date-time features (e.g., day, month, year).
   • Handle categorical variables where necessary.

Exploratory Data Analysis (EDA)

• Summary Statistics: Mean, median, min, max values.
• Visualizations:
  • Line plots for temperature trends.
  • Histograms to analyze data distributions.
  • Box plots to identify outliers.

Forecasting Model

• Linear Regression Model
  • Trained on selected features to predict temperature.
  • Evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE).

Results and Insights

• Identified general weather trends based on past data.
• Built a basic temperature forecasting model with reasonable accuracy.
• Visualized temperature variations and seasonal patterns.

Deliverables

• GitHub Repository with dataset, code, and analysis.
• EDA Report (Markdown/PDF) summarizing key insights.
• Graphs and charts for better data interpretation.

How to Run the Code

1. Clone the repository:

   git clone <repo_url>

2. Install required libraries:

   pip install -r requirements.txt

3. Open and run the Jupyter Notebook or Python script:

   jupyter notebook

4. Execute the EDA and forecasting steps as outlined in the script.

Submission

• The project is hosted on GitHub.
• The final report includes all analyses and findings.
• The repository link is shared for review and evaluation.

Weather Trend Forecasting - Advanced Analysis

Project Overview

This project involves analyzing global weather trends using machine learning techniques, anomaly detection, and forecasting models. The dataset used is GlobalWeatherRepository.csv. The project follows structured data science methodologies including data cleaning, exploratory data analysis (EDA), feature engineering, and model evaluation.

Objectives

• Perform Advanced EDA, including anomaly detection and feature importance analysis.
• Build and compare multiple forecasting models.
• Implement geospatial and climate analysis.
• Evaluate the impact of environmental factors on weather patterns.
• Document findings in a structured report and submit via GitHub.

Dataset

• Source: GlobalWeatherRepository.csv
• Features: Includes temperature, humidity, air quality, and geographical details.
• Target Variable: Forecasting weather trends over time.

Data Processing

1. Data Cleaning
   • Handle missing values and outliers.
   • Convert date-time fields to proper formats.
   • Standardize categorical values.
2. Feature Engineering
   • Create new features (e.g., temperature anomalies, seasonal trends).
   • Normalize numerical values where required.

Exploratory Data Analysis (EDA)

• Visualizations: Line plots, histograms, box plots.
• Anomaly detection using statistical and ML techniques.
• Feature importance ranking with SHAP and permutation methods.

Forecasting Models

• ARIMA: Time-series based prediction.
• Random Forest Regressor: Capturing nonlinear dependencies.
• LSTM (Deep Learning): Sequential learning for better accuracy.
• Ensemble Model: Combining multiple models to improve robustness.

Advanced Analysis

• Climate Patterns: Long-term trends in different regions.
• Environmental Impact: Correlation between air quality and weather.
• Spatial Analysis: Mapping temperature variations geographically.

Results and Insights

• Identified key trends affecting climate changes.
• Built robust forecasting models with accuracy comparisons.
• Generated interactive visualizations for deeper insights.

Deliverables

• GitHub Repository with all code, data, and documentation.
• Report (Markdown & PDF) summarizing findings.
• Presentation Slides with key results and recommendations.

How to Run the Code

1. Clone the repository:

   git clone <repo_url>

2. Install dependencies:

   pip install -r requirements.txt

3. Run Jupyter Notebook or Google Colab:

   jupyter notebook

4. Execute the main analysis script:

   python main.py

Submission

• The final project is uploaded to GitHub.
• The report and presentation include all key insights.
• Link to the repository is shared before the deadline.

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PM Accelerator Mission

This project aligns with the PM Accelerator's mission by leveraging data science to drive actionable insights in weather forecasting. Through rigorous analysis and predictive modeling, we provide meaningful interpretations that aid climate research and decision-making.