Data-Science-Project

Weather Dataset Exploration and Visualization

Objectives:

1.Download a weather dataset from Kaggle.

2.Explore various aspects such as Temperature, Relative Humidity,Visibility in KM and Temperature for Dew Point.

3.Create a correlation matrix that visualize these aspects.

Steps:

1.Data Collection:

Access the Weather dataset from Kaggle, which includes information on Temperature,Relative Humidity, Visibility in KM and Temperature for Dew Point.

Dataset Link: https://www.kaggle.com/datasets/ayushmi77al/weather-data-set-for-beginners?resource=download

2.Data cleaning and Preprocessing:

Clean the dataset by identifying and removing duplictes, identifying and handling missing values, addressing outliers by either removing or transforming them, removing irrelevant data and finally dealing with any invalid or inconsistent data.

3.Exploratory Data Analysis (EDA):

Analyze the data to identify key factors contributing to weather.

The common EDA techniques include the use of summary statistics, visualization and correlation metrices.

In summary of statistics, I performed and calculated Mean, Mode and Median.

In visualization techniques, I visualize the data using Scatter Plot, Line chart, Histogram and Box-PLot and perform the relationship between Temperature and Relative Humidity, Visibility in KM and Temperature for Dew Point respectively.

Finally, I examine the correlations between variables using correlation matrices.