- Walmart Data-Retail Analysis in PYTHON
Overview:
One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. The business needs to understand the sales in their 45 stores and the impact of holidays and other factors. So, the objective of this project is to do the data analysis and provide insights to the stakeholders.
The data was available for years 2010, 2011 and 2012.
The analysis answered below key questions:
- Which store is performing well and has a good growth rate? How significant are they from one another?
- How are sales at different time periods?
- Are the holidays affecting the sales in the stores? If so, how significant compared to non-holiday days?
- Did the factors: temperature on day of sale, fuel price in the region, prevailing consumer price index and prevailing unemployment rate have an impact on the sales?
- Customer Churn Analysis in PYTHON
Understanding Customer Churn:
Customer churn refers to when a customer ends his or her relationship with a business.
Acquiring new customers can be several times more expensive than selling to existing ones. Understanding what drives churn and why customers are churning is crucial in maintaining high retention rates. Being able to accurately identify those customers at high risk of churning, may help us to create appropriate marketing strategies and retain our customers.
- Time-Series Analysis and Forecasting product sales in PYTHON
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post. We will demonstrate different approaches for forecasting retail sales time series.
- Customer Segmentation using RFM modeling in PYTHON
RFM (Recency, Frequency, Monetary) analysis is a behavior-based approach grouping customers into segments. It groups the customers on the basis of their previous purchase transactions. How recently, how often, and how much did a customer buy. RFM filters customers into various groups for the purpose of better service. It helps managers to identify potential customers to do more profitable business. There is a segment of customer who is the big spender but what if they purchased only once or how recently they purchased? Do they often purchase our product? Also, It helps managers to run an effective promotional campaign for personalized service.
- Housing Prices Data Analysis in EXCEL
Overview:
This is a project to determine the factors driving house prices, which will help the team at an investment bank to understand how to allocate funds earmarked for investment into mortgage-backed securities. The data with 79 explanatory variables describing every aspect of residential home, was used, to generate descriptive statistics. The correlation and regression analysis is done to figure the most impacting variables on house price and then hypothesis testing is performed on those to further emphasize the significance of their effect on the prices.
- Lariat Fleet Planning in EXCEL
Project: Lariat Fleet Planning: This Excel model has been built to analyze 2018 rental data of Lariat fleet and draw insights of the past performance. The recommendations were made with strategies to optimize revenue for 2019. In this interactive model, they can simulate the strategic scenarios by updating the relevant parameters as instructed, to see how the revenue changes. The deck has been created for a quick presentation of insights and recommendations to the executives of Lariat. This will help them to make better decisions and planning for 2019.