Project 2 - EDA on Vehicle Insurance Customer Data

Aim The aim of this Power BI project is to assist ABC Company in comprehending their employee data to extract valuable insights, enhance decision-making, and improve overall HR management. Introduction: This project leverages Power BI to analyze and visualize HR data for ABC Company. The dashboard provides a comprehensive view of various HR metrics, enabling stakeholders to monitor trends, identify patterns, and make data-driven decisions. The dashboard includes visualizations on employee count trends, gender distribution, age distribution, and department and country-wise employee counts. Additionally, dynamic filtering through slicers and a reset functionality for ease of use have been implemented. Problem Statement ABC Company is facing challenges in comprehending their employee data to extract valuable insights. The lack of clear and interactive visualizations hampers their ability to monitor HR metrics effectively, leading to potential issues in workforce planning, diversity management, and strategic HR decision-making. Methodology • Data Collection: Gather HR data from ABC Company's internal systems, including employee demographics, department assignments, and geographical information. • Data Cleaning: Ensure the data is clean and consistent, addressing any missing values or discrepancies. • Data Modeling: Structure the data appropriately within Power BI, establishing relationships between tables for seamless analysis. • Dashboard Development: Create visualizations to represent key HR metrics, including: • Year-on-year trends in employee count • Gender distribution using cards • Age-wise distribution using bar charts • Department-wise and country-wise employee counts • Interactive Elements: Implement slicers for dynamic filtering by department, country, and year. Add a reset icon with a bookmark to reset the filters. • Testing and Validation: Test the dashboard for accuracy and usability, ensuring it meets the requirements of ABC Company's HR team. Analysis: 1. Employee Data: Contains details of employees including ID, name, gender, age, department, and country. 2. Department Data: Lists all departments within ABC Company. 3. Country Data: Lists all countries where ABC Company operates. 4. Yearly Employee Data: Aggregated data showing employee counts per year. Insights: 1. Year-on-Year Employee Trends: Identified trends in employee growth or decline over the years, helping in workforce planning. 2. Gender Distribution: Provided a clear view of gender diversity within the company, aiding in diversity and inclusion initiatives. 3. Age Distribution: Highlighted age demographics, supporting strategies for employee engagement and retention. 4. Department-wise Analysis: Showed the distribution of employees across different departments, useful for resource allocation and departmental growth analysis. 5. Country-wise Analysis: Revealed geographical distribution of employees, assisting in global workforce management. Recommendations: 1. Enhance Diversity Programs: Based on gender distribution insights, implement or enhance programs aimed at promoting gender diversity. 2. Targeted Engagement Strategies: Develop age-specific engagement and retention strategies to address the needs of different age groups within the workforce. 3. Strategic Workforce Planning: Utilize year-on-year trends to forecast future hiring needs and plan accordingly. 4. Resource Allocation: Use department-wise insights to allocate resources effectively and support under-resourced departments. Conclusions: The Power BI dashboard developed for ABC Company provides a comprehensive and interactive analysis of their HR data. By visualizing key metrics and enabling dynamic filtering, the dashboard enhances the company's ability to monitor and manage their workforce effectively. The insights derived from the dashboard support data-driven decision-making, contributing to improved HR management and strategic planning.

Goal of the Project: The primary goal of this project is to explore and analyze the factors that influence wine quality ratings, specifically focusing on the differences between red and white wines. The analysis aims to determine how attributes such as alcohol content, acidity, and other chemical properties relate to wine ratings, as well as to investigate the relationships between sweetness and quality. Ultimately, this project seeks to provide insights that could be beneficial for winemakers and consumers in understanding wine quality. Process 1. Data Loading and Exploration: - The project begins with loading the dataset and displaying the data for both white and red wines. - Initial exploration includes obtaining the size of each wine type and checking for null values. 2. Data Cleaning: - Duplicate entries are identified and removed from both datasets to ensure data integrity. - The number of unique values in all features is calculated and displayed. - Null values are removed to maintain a clean dataset. - Outliers are detected and removed using statistical methods to prevent skewed results in analysis. 3. Descriptive Statistics: - The mean density of both wine types is calculated for comparison. - A new column is created based on acidity levels derived from pH values, categorizing wines into different acidity levels. 4. Statistical Analysis: - Correlation analysis is performed to examine the relationship between citric acid content and pH levels. - Regression analysis is conducted to determine the influence of citric acid on pH. - The project assesses how wines with higher alcohol content correlate with quality ratings. - A group-by operation is utilized to find the mean quality ratings for each acidity level. 5. Hypothesis Testing: - The analysis investigates whether certain types of wine (red or white) are associated with higher quality ratings. - Confidence intervals are calculated to analyze differences in proportions of wines with a specific quality rating. 6. Data Visualization and Summary: - Visual representations are generated to illustrate key findings and relationships. - The project summarizes the insights gained from the analysis, particularly focusing on sweetness and its impact on quality ratings. Features - Quality Ratings: The dependent variable representing the quality of the wine, with possible ratings from 0 to 10. - Wine Type: A categorical variable indicating the type of wine, either red or white. - pH Levels: A continuous variable used to infer the acidity and sweetness of the wine. - Alcohol Content: A continuous variable indicating the percentage of alcohol present in the wine. - Citric Acid: A continuous variable representing the citric acid content in the wine, which may influence its taste and quality. - Density: A continuous variable reflecting the density of the wine, which can provide insight into its composition. - Acidity Level: A new categorical variable derived from pH levels, divided into five groups: `High`, `Moderately_High`, `Medium`, `Low`, and `Very_Acidic`. Expected Outcomes: - Insights into the differences in wine quality ratings between red and white wines. - Statistical evidence regarding the relationship between alcohol content and quality ratings. - A clearer understanding of how citric acid and pH levels correlate and affect wine quality. - Conclusions on whether sweeter wines receive better ratings and how acidity influences perceived quality.

Goal of the Project The primary objective of this project is to analyze the factors influencing insurance charges and to create a predictive model that estimates these charges based on various independent variables. This involves examining the relationships between features such as age, sex, BMI, number of children, smoking status, and region, and how they collectively impact insurance costs. Project Outline 1. Data Explanation and Supervised vs. Unsupervised Learning - Data Description: The dataset consists of various features, including: - Independent Variables: Age, Sex, BMI, Number of Children, Smoking Status, Region. - Target Variable: Insurance Charges. - Learning Type: This analysis falls under supervised learning because it utilizes labeled data (insurance charges) to train a predictive model. 2. Estimate Minimum Sample Size for 99?curate Predictions To ensure that the predictions are accurate with a confidence level of 99% and a precision of 0.02, the minimum sample size can be estimated using statistical methods. This calculation helps determine how many data points are required for reliable predictions. 3. Data Cleaning - Null Values: Evaluate the dataset for any missing values and address them by imputation or removal. - Duplicates: Identify and eliminate any duplicate entries in the dataset to maintain data integrity. - Outliers: Analyze the data for outliers that could skew results and consider appropriate handling techniques. - Categorical Encoding: Convert categorical string variables into numerical format to facilitate analysis and modeling. 4. Statistical Independence of Sex and Smoking Utilize statistical tests (such as the Chi-Square test) to determine if there is a significant association between sex and smoking status. A p-value below 0.05 indicates that the variables are statistically dependent. 5. Independence of Regressor Variables Check for multicollinearity among independent variables using correlation matrices or variance inflation factors (VIF). Independent variables should not exhibit strong correlations, as this could affect model performance. 6. Dependency Between Response and Regressors Analyze the relationship between the target variable (insurance charges) and independent variables through statistical techniques such as correlation analysis and regression modeling. This step helps in understanding which features significantly impact the target variable. 7. Predicting the Regression Line Develop a regression model using appropriate techniques (e.g., linear regression) to create a regression line. This line will be utilized for predicting insurance charges based on the independent variables. 8. Model Accuracy Prediction Evaluate the regression model's performance using metrics such as R-squared, Mean Absolute Error (MAE), or Root Mean Square Error (RMSE). These metrics provide insights into how well the model predicts insurance charges. 9. Predicting Insurance Charges Use the regression model to estimate insurance charges for a specific set of inputs, such as: - Age: 29 - Sex: Female - BMI: 28 - Number of Children: 1 - Smoking Status: Yes - Region: Southeast 10. Percentage of Error in the Regression Model Calculate the percentage of error in the predictions to assess the model's accuracy. This can be done by comparing predicted values to actual values and determining the average error percentage. 11. 95% Confidence Interval for Average Charges Compute the 95% confidence interval for the average insurance charge, providing a range within which the true mean of insurance charges is expected to fall. This statistical measure adds credibility to the prediction results. Conclusion: Summarize the findings from the analysis, including key insights about the relationship between the independent variables and insurance charges, the effectiveness of the predictive model, and potential areas for future research.

I have attached my project file and documentation file below.

i have attaached my project file below.

i have attached project 1 answer file below.

i have attached my answers for all questions in sqlfile and excelfile and ipynb file and some screenshots. and then i tried to get exact percentage for Q .16. but i could get same result as shown in chellange question page. also i used over partition by function it shows error. kindly provide me solution for that question. 

answers i have attached sql file and with screenshots after execution. i have ried my best to answer all questions.

project file attached below

in this, womens are getting away from science studies and selecting in engineering and doctor courses. and mostly varies in salary of men and women. thats it.    my project file attached below.

vba code below, to convert selected data into pdf from applicant data : Sub SaveRangeAsPDF() Dim saveLocation As StringDim ws As WorksheetDim rng As Range saveLocation = "C:\Users\ADMIN\Documents"Set ws = Sheets("Sheet1")Set rng = ws.Range("A1:H20") rng.ExportAsFixedFormat Type:=xlTypePDF, _Filename:=saveLocation End Sub…

answers in excel file attached below