UNDERSTANDING YOUR DATA: THE ESSENTIALS OF EXPLORATORY DATA ANALYSIS

Exploratory Data Analysis (EDA) is an essential phase in data science that allows you to better understand your data, identify trends, and obtain insights.
EDA analyzes and visualizes data to uncover relationships, detect abnormalities, and verify data quality.

Importance of EDA;
1.Summarize main characteristics of the data
2.Gain better understanding of the data set
3.Uncover relationships between different variables
4.extract important variables for the problem that is trying to be solved.

1. Outlier Detection- Identifies odd data points that may impact the analysis.
2. Error detection - Assists in identifying and correcting errors in data.

Tools and Techniques;

1. Jupyter Notebook: An interactive environment for EDA (has tools like pandas and NumPy)
2. Visualization Libraries: Tools like Matplotlib, Seaborn, and SciPy for creating visualizations.
3. Summary Statistics: Key metrics to summarize data (e.g., mean, standard deviation).
4. Data Transformation: Techniques like normalization to improve data quality.

Steps involved in EDA;

1. Data Collection - Collecting data for your analysis.
   Collect data from several sources e.g., databases.
   Ensure that the data is correct, complete, and relevant to your situation.

2. Data Cleaning – Involves Preparing data by removing inconsistencies and inaccuracies.
   A) Remove duplicates: Make sure each record is unique and meaningful
   

# Check for duplicate records
duplicate_mask = data.duplicated()
# Count the number of duplicate records
num_duplicates = duplicate_mask.sum()
print(f"Number of duplicate records: {num_duplicates}")
# View duplicate records
if num_duplicates > 0:
    print("Duplicate records:")
    print(data[duplicate_mask])
# Optionally: Remove duplicate records
# weather_df = weather_df.drop_duplicates()
# Save the cleaned dataset (if duplicates were removed)
# weather_df.to_csv('cleaned_weather_dataset.csv', index=False)

B) Handle Missing Values: Determine whether to eliminate or fill in missing data.

# Check for null & missing values
data.isnull().sum()

C) Standardizing data; Ensuring Consistency in data formats
D) Correcting errors; Fixing any data entry mistakes

3.Data visualization
Involves using visual tools to investigate the distribution, trends, and relationships in data.
A) Histograms illustrates the distribution of a single variable.
B) Box plots- used to highlight the distribution and identify outliers.
C) Scatter plots- Investigate the correlations between two variables.
D) Bar charts allow you to compare category data.

4.Statistical analysis.
Using statistical measures to summarize the data and analyze its key properties.

A) Summary Statistics: Determine the mean, median, standard deviation, and other metrics.
` Data.describe()

`
B) Correlation Analysis: Determine the relationship between variables using heat map plot

#heatmaps to identify relationships between different weather parameters.
correlation_matrix = numeric_data.corr()
plt.figure(figsize=(10, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()

5.Interpretation and Insight
Drawing conclusions and produce insights from the analysis.
A) Interpret visualizations and statistics. Understand the data's patterns, trends, and relationships.
B) Generate hypotheses: Create hypotheses based on the EDA to guide future analysis.
C) Document Findings: Clearly document findings, any effects, and any issues discovered in the data.