Weather dataset

Dataset Overview

Dataset Name

Walmart Sales Data Analysis - Weather Dataset

Dataset Description

• Type: Time‑series dataset
• Content: Hourly weather condition information, including temperature, dew‑point temperature, relative humidity, wind speed, visibility, atmospheric pressure, and weather condition.
• Time Span: January 2012 – December 2012
• Format: CSV file
• Tool: Analysis performed with Python's pandas library

Dataset Structure

• Rows: 8,785 rows
• Columns: 8 columns
• Column Details:
  • Date/Time: Timestamp of the recorded condition
  • Temp_C: Temperature
  • Dew Point: Dew‑point temperature
  • Wind Speed_km/h: Wind speed
  • Visibility_km: Visibility
  • Press_kPa: Atmospheric pressure
  • Weather: Weather condition

Analysis Objectives

• The primary goal is to predict future weather conditions by analyzing weather patterns and variables.

Analysis Methods

1. Data Cleaning: Inspect and handle NULL and missing values.
2. Feature Engineering: Generate new columns from existing ones.
3. Exploratory Data Analysis (EDA): Address the questions and objectives listed in the project.

Analysis Tasks

1. Predictive Modeling: Build models based on historical data to forecast future weather conditions.
2. Descriptive Analysis: Summarize and describe basic dataset characteristics such as average temperature, precipitation level, wind speed, humidity, etc.

Analytical Questions

• Q1–Q13 enumerate specific analytical questions covering weather condition counts, column renaming, statistical calculations, and conditional queries.

Model

• The data model is used to predict future weather conditions.