Exploratory Data analysis (EDA)

is an approach to analyse data to: Summarize main characteristics of the data so as to gain better understanding of the data set and Uncover relationships between different variables.
In this analysis I am used the Weather dataset that was downloaded from Kaggle. I Performed Exploratory Data Analysis (EDA) to uncover interesting patterns, insights, and potential anomalies in dataset.
To do that I will, undertook the following tasks, i.e. Data Overview and Cleaning, Statistical Summary, Data Visualization, Creation of correlation matrices and heatmaps and then Analysed any trends or patterns observed in the data.

1. Data Overview and Cleaning
• Dataset Characteristics: The dataset consists of multiple records detailing weather conditions, including features like temperature, dew point, humidity, wind speed, visibility, pressure, and weather descriptions.
• Missing/Null Values: The analysis identified no missing or null values in the data.
df.isna().sum().sum()
• Duplicate Records: I addressed duplicate records, ensuring the dataset used for analysis was free from redundant entries. This step was crucial for maintaining the accuracy of statistical analyses and visualizations.

#detecting duplicates
#We used "Date/Time" because the dataset shouldn’t have weather patterns for the same date and time.
df["Date/Time"].duplicated().sum()

2. Statistical Summary
• Descriptive Statistics: I obtained a statistical summary of key numerical features such as temperature, humidity, wind speed, and visibility. These included measures of central tendency (mean, median) and dispersion (standard deviation, range).
• Outliers:
We can use Box Plots or Scatter Plots to identify outliers. In this analysis I used Box plots and Outliers are typically shown as points outside the “whiskers” of the box plot.
I identified significant outliers especially in Wind speed, Visibility and Pressure.

3. Data Visualization
For data visualisation, I; 1) created visualizations to show the distribution of key weather parameters (e.g., temperature, humidity, wind speed) 2) Plotted time series graphs to visualize trends over time which will highlighted notable patterns or seasonal variations and 3) Created correlation matrices and heatmaps to identify relationships between different weather parameters.

• Distribution Visualizations: I visualized the dataset to show the distribution of key weather parameters. Histograms and box plots were used to illustrate how data like temperature, humidity, and wind speed are distributed.
• Time Series Analysis: Time series plots were generated to explore trends over time, highlighting seasonal variations and patterns. The notebook effectively visualized how temperature and humidity fluctuate across different months and seasons.
• Correlation and Heatmaps: Correlation matrices and heatmaps were used to explore relationships between different weather parameters. Strong correlations were observed between temperature and dew point, and between wind speed and pressure, among others.
4. Weather Patterns and Trends
• Seasonal Trends: The analysis uncovered clear seasonal trends in temperature and humidity, with distinct patterns observed in different months. For example, winter months showed lower temperatures and higher humidity levels, while summer months exhibited the opposite.
5. Insights and Conclusions
• Key Insights:
o The dataset revealed strong seasonal patterns, particularly in temperature and humidity, which are crucial for understanding local climate behavior.
o The correlation between weather parameters, such as temperature and dew point, provides valuable insights for predicting one parameter based on the others.
o The identification of outliers and anomalies can help in forecasting extreme weather events, which are crucial for preparedness and disaster management.
• Practical Applications:
o The insights gained from this analysis can be used to improve weather prediction models, particularly in forecasting temperature and humidity based on historical patterns.
o Understanding the correlations between different weather parameters can enhance predictive analytics in agriculture, tourism, and event planning.
6. Recommendations for Further Analysis
• Deeper Anomaly Analysis: A more detailed investigation into the identified anomalies could be beneficial. Understanding the causes of these outliers could provide insights into rare weather events.
• Additional Data: Incorporating more features, such as geographical data (e.g., latitude and altitude), could help refine the analysis and improve the accuracy of predictions.
• Predictive Modeling: Developing machine learning models using this dataset could be the next step. These models could be trained to predict future weather patterns based on the insights gained from this EDA.