Data analyses 

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Payal Gawande  • Peerlist

Data Analytics professional at Schenider Electric having 2+ years of experience with Excel,SQL, Python,Tableau

<h1 class="rich-heading">DailyUber provides the 2016 data. By using this data find out the demand and supply Analysis gap of the cab. The main objective of this project is to analyse the data on Uber Request data. Through the analysis of data, we can find some important insights.</h1>App Openings (Eyeballs) = Customers who launch the app looking for riders. It is a good measure of demand Zeroes = Customers who open the app and see no cars in the area. Request = Customers who make requests for a car. Completed Trip = The point from when a customer is picked

Uber Case Study

Background and Problem:<ol><li>Maven Marketing conducted a marketing campaign to engage potential and inactive customers.</li><li>Data from 2,240 customers were analyzed to identify characteristics of the average customer and factors affecting web channel sales.</li><li>The goal is to collect and clean the data, analyze it using Excel's Pivot Table feature, and provide recommendations based on the findings.</li><li>The project scope includes data collection, cleaning, analysis, and recommendations for marketing strategies.</li></ol>Methodology and Project Scope:<ol><li>Data collection: Gather data from 2,240 customers who participated in the marketing campaign.</li><li>Data cleaning: Clean the data to remove duplicates, handle missing values, and ensure data consistency.</li><li>Analysis: Utilize Excel's Pivot Table feature to analyze customer profiles, campaign success, channel performance, and revenue improvement.</li><li>Recommendations: Based on the analysis, provide actionable recommendations for marketing strategies to improve campaign effectiveness and increase revenue.</li></ol>Goals and Key Performance Indicators (KPIs):<ol><li>Increase revenue: Measure the effectiveness of the marketing campaigns in generating revenue.</li><li>Improve marketing strategies: Identify areas for improvement and suggest strategies to target potential customers.</li><li>Key Performance Indicators (KPIs): Track conversion rate, customer acquisition cost (CAC), return on investment (ROI), click-through rate (CTR), and customer lifetime value (CLV) to evaluate campaign performance.</li></ol>Concepts Used:<ol><li>Pivot Tables: Analyze and summarize the data to gain insights into customer profiles, campaign success, and channel performance.</li><li>Data cleaning and pre-processing: Ensure data accuracy and consistency by removing duplicates and handling missing values.</li><li>Mathematical operations: Calculate metrics such as conversion rate, CAC, ROI, CTR, and CLV to evaluate campaign effectiveness.</li><li>Data visualization: Present findings using charts and graphs to aid in data interpretation.</li><li>Data dictionaries: Document data definitions and structures for easy reference and understanding.</li></ol>Conclusion: By analyzing the marketing campaign data using Excel's Pivot Table feature, Maven Marketing can gain insights into customer profiles, campaign success, and channel performance. These insights can be used to improve marketing strategies, increase revenue, and target potential customers more effectively. Key performance indicators such as conversion rate, CAC, ROI, CTR, and CLV will help measure the success of the campaigns and guide future marketing efforts. The project's findings and recommendations will assist Maven Marketing in optimizing their marketing strategies and achieving better results.

Marketing Campaign Results

The dataset obtained from Rotten Tomatoes, consisting of ratings and reviews for over 15,000 movies, presents an opportunity for comprehensive analysis and valuable insights into the movie industry. With the dataset's extensive information on movie attributes, ratings, and audience feedback, several analysis questions and areas of exploration can be recommended:<ol><li>Distribution of films by rating and primary genre: Analyze the frequency and proportions of movies across different ratings and primary genres. This analysis would provide an understanding of the overall distribution of films based on ratings and genres.</li><li>Percentage of films with Certified Fresh and Rotten Tomatometer ratings: Calculate the percentage of movies that received the Certified Fresh and Rotten Tomatometer ratings. This analysis would offer insights into the critical reception and overall quality of films in the dataset.</li><li>Trend analysis of new film releases over time: Explore how the volume of movie releases varies over time, particularly by month and year. Identify the months or years with the highest number of new film releases, providing insights into trends and patterns in the industry.</li><li>Average Tomatometer ratings by Studio: Compare the average Tomatometer ratings across different movie studios. Identify which studios consistently produce the highest and lowest rated films, on average. This analysis would shed light on the performance and reputation of studios in delivering critically acclaimed movies.</li><li>Discrepancies between Tomatometer and audience ratings: Analyze and compare the Tomatometer ratings with the audience ratings for movies. Identify films with significant differences in ratings between critics and the general audience. This analysis would highlight cases where critical opinions and audience preferences diverge.</li><li>Exploration of critics consensus rating language: Analyze the language used in the critics consensus ratings. Identify the most frequently used language in these ratings, providing insights into the critical evaluation and description of movies.</li></ol>In conclusion, the dataset from Rotten Tomatoes offers a valuable resource for conducting various analyses and gaining insights into the movie industry. By exploring movie ratings, genres, release trends, studio performances, audience discrepancies, and critic consensus language, this analysis can inform decision-making processes related to movie production, marketing, and distribution, benefiting the industry as a whole.

Movie Rating

Content This dataset comprises of prices and price % change for coarse wool, copra, cotton, fine wool, hardlog, hard sawnwood, hide, plywood, rubber, softlog, soft sawnwood and wood pulp.<ul><li>Load the dataset into a data frame using Pandas</li><li>Explore the number of rows &amp; columns, ranges of values etc.</li><li>Handle missing, incorrect and invalid data</li><li>Perform any additional steps (parsing dates, creating additional columns)</li><li>Compute the mean, sum, range and other interesting statistics for numeric columns</li><li>Explore distributions of numeric columns using histograms etc.</li><li>Make a note of interesting insights from the exploratory analysis</li></ul>Given Data :-Coarse wool PriceCoarse wool price % ChangeCopra PriceCopra price % ChangeCotton PriceCotton price % ChangeFine wool PriceFine wool price % ChangeHard log PriceHard log price % Change...Plywood PricePlywood price % ChangeRubber PriceRubber price % ChangeSoftlog PriceSoftlog price % ChangeSoft sawnwood PriceSoft sawnwood price % ChangeWood pulp PriceWood pulp price % Change and Month Conclusion and Insigts :-1 Suppose we are planning some bussines which requrired my own raw material than this information will help<ul><li>The given information includes the prices of various raw materials such as Coarse wool, Copra, Cotton, Fine wool, Hard log, Hard sawnwood, Hide, Plywood, Rubber, Softlog, Soft sawnwood, and Wood pulp.</li><li>The respective prices of these raw materials are 320, 471, 1.29, 469, 275, 880, 76, 650, 0.89, 158, 230, and 880 (rounded figures).</li><li>Analyzing the trends in the prices of these raw materials can help us understand which crops are profitable to grow in the future.</li><li>Based on this information, we can forecast the future prices of these raw materials and make a budget accordingly.</li></ul>2 Costing accoding to time period<ul><li>For instance, if a business requires a particular raw material during a specific month, analyzing the historical trends in the prices of that material can help in forecasting its future prices. Based on the forecasted prices and the budget allocated for that particular period, the business can plan for the procurement of the raw material.</li></ul>3 Overlapping ObservationOverlapping of raw materials in the price chart does not necessarily mean that the requirement for all materials is the same throughout the year. Each raw material has its unique demand and supply factors that affect its price, and the demand for different materials can vary depending on various factors such as seasonal changes, industry trends, and market conditions.For example, the demand for cotton may be higher during the summer months when clothing production increases, while the demand for rubber may be higher during the rainy season when there is an increased need for tires and other rubber products. Similarly, the demand for wood products may be higher during the construction season, while the demand for animal hides may be higher during the winter months when the demand for leather products increases.Therefore, it is important to analyze the demand and supply factors for each raw material separately to understand its unique requirements and forecast its future prices accordingly. This information can help businesses make informed decisions about their procurement and production strategies and ensure that they have sufficient raw materials to meet their production needs.<h1 class="rich-heading">THANK YOU FOR VISITING</h1>Please share your thoughts, it will be helpful for me

EDA-Agricultural Raw Material

I am pleased to present my first Excel analysis report, which examines Pizza Place Sales based on Kaggle<a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="Pizza%20Place%20Sales.pptx">(</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.kaggle.com/datasets/mysarahmadbhat/pizza-place-sales">https://www.kaggle.com/datasets/mysarahmadbhat/pizza-place-sales</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="Pizza%20Place%20Sales.pptx">).</a> As you will see, this report provides a comprehensive overview of the key trends and patterns that emerged from the data, as well as recommendations for how to leverage this information to achieve promote some low demand product to promotion, understanding sales, budget tracking and profit analyses. Throughout this analysis, I utilized a range of Excel tools and techniques, including massive used of Pivot Table and charts to visualize data as well as I used sum, average, min, max , Lookup and many more function. By combining these tools with my analytical skills and expertise, I was able to extract meaningful insights from the data and create a report that is both informative and actionable. I hope that you find this report to be insightful and engaging, and that it provides a valuable starting point for further analysis and decision-making. Thank you for your attention, and please don't hesitate to contact me if you have any questions or feedback.For more good experiance of data try to check PPT and EXCEL together its will help you to understand step by step analyses. Sincerely, Payal Gawande.