Ticket Description: Perform Exploratory Data Analysis (EDA)
Title: Perform Exploratory Data Analysis (EDA)
Description:
The purpose of this ticket is to perform a thorough Exploratory Data Analysis (EDA) on the provided dataset for the fraud detection project. EDA will help in understanding the data distribution, identifying patterns, and detecting any anomalies or missing values. This analysis is crucial for informing the feature engineering and model selection stages.
Details:
Objective: Analyze the dataset to uncover initial insights, understand the data structure, and identify potential data quality issues.
Dataset: Fraud Detection in Electricity and Gas Consumption Challenge dataset from Zindi link.
Steps:
Data Loading:
Load the dataset into the analysis environment (e.g., Jupyter Notebook, RStudio).
Data Overview:
Display the first few rows of the dataset.
Summarize the dataset to understand the number of observations, features, and their types (numeric, categorical, etc.).
Data inspecting:
Check for missing values
Identify data quality issues (e.g., duplicate entries, incorrect data types).
Descriptive Statistics:
Calculate basic statistics (mean, median, mode, standard deviation) for numeric features.
Summarize the distribution of categorical features.
Visualization:
Create histograms and box plots for numeric features to visualize their distributions.
Create bar plots for categorical features to understand their frequency distributions.
Plot correlations between numeric features to identify relationships.
Use scatter plots and pair plots to explore relationships between features.
Anomaly Detection:
Identify and document any outliers or anomalies in the data.
Feature Analysis:
Investigate potential features that may contribute to detecting fraud.
Document observations that could inform feature engineering.
EDA Documentation:
Compile a comprehensive report summarizing the findings of the EDA.
Include visualizations, descriptive statistics, and insights derived from the analysis.
Ticket Description: Perform Exploratory Data Analysis (EDA)
Title: Perform Exploratory Data Analysis (EDA)
Description: The purpose of this ticket is to perform a thorough Exploratory Data Analysis (EDA) on the provided dataset for the fraud detection project. EDA will help in understanding the data distribution, identifying patterns, and detecting any anomalies or missing values. This analysis is crucial for informing the feature engineering and model selection stages.
Details:
Steps:
Data Loading:
Data Overview:
Data inspecting:
Descriptive Statistics:
Visualization:
Anomaly Detection:
Feature Analysis:
EDA Documentation:
Attachments:
Comments:
Outcome Format: The outcome should be a well-documented notebook (e.g., Jupyter Notebook) and an .md file summarizing the key findings: