Exploratory Data Analysis

Finished on: DATE

Objective

The objective of this portfolio website is to showcase my expertise in Exploratory Data Analysis (EDA) through a detailed case study using the loan_data.csv dataset. This dataset includes information about loan applications such as loan amounts, interest rates, loan purposes, and borrower details. The goal is to demonstrate my ability to preprocess, clean, and analyze data to uncover meaningful insights that can drive business decisions.

Objective

Assess the volume and dimensions of the dataset to understand its scope.

Details

Start by loading the loan_data.csv file and examining its structure. Check the number of rows and columns to get a sense of the dataset's size. This initial step helps in planning the subsequent analysis and resource allocation.

Tools/Methods

Use functions like shape in pandas to get the number of records and attributes.

Objective

Review and understand all the attributes (columns) present in the dataset to set the context for the analysis.

Details

List and describe each column, noting its name, type, and a brief description of what it represents. This helps in understanding the data content and its potential use in analysis.

Tools/Methods

Utilize info() and describe() methods in pandas for an overview and statistical summary of the attributes.

Objective

Identify and eliminate columns that do not contribute to the analysis, ensuring focus on relevant data.

Details

Evaluate each column's relevance to the analysis objectives. Remove columns that are redundant, have too many missing values, or are not useful for predictive modeling or descriptive statistics.

Tools/Methods

Perform manual inspection and use domain knowledge to filter out unnecessary columns.

Objective

Verify data types of each attribute and perform necessary conversions to ensure consistency and accuracy.

Details

Check if each column has the correct data type (e.g., numerical, categorical, date). Convert columns to appropriate types if they are incorrectly formatted (e.g., convert numerical strings to integers).

Tools/Methods

Use astype() in pandas for type conversion and validate using info().

Objective

Detect missing data points and strategize appropriate methods to handle them.
Implement techniques such as imputation or removal to address missing data issues.

Details

Identify columns with missing values and assess the extent of the missing data. Understanding the pattern and amount of missing data is crucial for deciding how to handle it.
Decide on a strategy for each column with missing data, such as imputing with mean/median/mode, forward/backward filling, or dropping rows/columns. Justify the chosen method based on the data context and analysis requirements.

Tools/Methods

Use isnull() and sum() in pandas to quantify missing values.

Use fillna(), dropna(), and other imputation techniques in pandas.

Objective

Identify outliers that could potentially skew the analysis and decide on strategies to handle them.

Apply methods to manage or transform outliers to maintain data integrity.

Details

Detect extreme values in numerical columns using statistical methods or visualization techniques. Determine the impact of outliers on the analysis and whether they should be transformed or removed.

Choose appropriate techniques to handle outliers, such as capping, transformation, or removal. Ensure that the chosen method preserves the validity of the data.

Tools/Methods

Use box plots, z-scores, or IQR methods to identify outliers.

Apply capping, log transformation, or winsorization as needed.

Univariate Analysis

Objective

Analyze individual attributes to understand their distribution, central tendency, and variability.

Details

Explore each variable separately using descriptive statistics and visualizations like histograms, box plots, and summary statistics. This helps in understanding the underlying distribution and characteristics of each attribute.

Tools/Methods

Use pandas for summary statistics and matplotlib/seaborn for visualizations.

Bivariate Analysis

Objective

Explore relationships between pairs of variables to uncover potential interactions.

Details

Analyze the interaction between two variables using scatter plots, cross-tabulations, and correlation coefficients. This step helps in understanding the relationships and dependencies between variables.

Tools/Methods

Use scatter plots, bar plots, and correlation matrices.

Multivariate Analysis

Objective

Investigate complex interactions between multiple variables to reveal deeper insights.

Details

Analyze how multiple variables interact with each other using techniques like pair plots, multiple regression analysis, and advanced visualization methods. This provides a comprehensive view of the data dynamics..

Tools/Methods

Use pair plots, multi-dimensional scaling, and other advanced statistical methods.

Data Set

Age	Daily.Steps	Activity.Level
25	5000	Moderate
thirty	8000	High
35	7000	Moderate
40	6000	Low
45	NaN	Low

Exploratory Data Analysis

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