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analyzing-the-email-opening-rates

Before building an email marketing campaign, it’s important to define your goals so you know if your campaign will be a success. One of the most vital factors to consider is how many people read and engage with your emails. This is a great indicator to show if your efforts and resources are worth the investment.

analyzing-the-imdb-movie-dataset

The Internet Movie Database (IMDb) is a website that serves as an online database of world cinema. This website contains a large number of public data on films such as the title of the film, the year of release of the film, the genre of the film, the audience, the rating of critics, the duration of the film, the summary of the film, actors, directors and much more. Faced with the large amount of data available on this site, I thought that it would be interesting to analyze the movies data on the IMDb website between the year 2000 and the year 2017.

analyzing-the-prices-of-avocado

The Date of the observation AveragePrice - the average price of a single avocado type - conventional or organic year - the year Region - the city or region of the observation Total Volume - Total number of avocados sold 4046 - Total number of avocados with PLU 4046 sold 4225 - Total number of avocados with PLU 4225 sold 4770 - Total number of avocados with PLU 4770 sold Acknowledgements Many thanks to the Hass Avocado Board for sharing this data

anonymous-blogging-website

Built on Laravel.

attrition-analysis-on-the-hr-department

The rate of attrition or the inverse retention rate is the most commonly used metric while trying to analyze attrition. The attrition rate is typically calculated as the number of employees lost every year over the employee base. This employee base can be tricky however. Most firms just use a start of year employee count as the base. Some firms calculate it on a rolling 12 month basis to get a full year impact. This ratio becomes harder to use if your firm is growing its employee base. For example, let's say on Jan 1st of this year there were 1000 employees in the firm. Over the next 12 months we've lost 100 employees. Is it as straight forward as a 10% attrition rate. Where it gets fuzzy is how many of those 100 employees that were lost were in the seat on Jan 1st. Were all the 100 existing employees as of Jan 1st or were they new hires during the year that termed. Hence the attrition rate must be looked at in several views.

building-a-movie-recommender-systems

Recommender System is a system that seeks to predict or filter preferences according to the user's choices. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.

clustering-analysis-on-mall-customers

Problem Statement: This data set is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis . I will demonstrate this by using unsupervised ML technique (KMeans Clustering Algorithm) in the simplest form.You are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Problem Statement You own the mall and want to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly.

country_guide

Fetching api and showing the details of a country or a city.

cricapi

currency_convertor

It fetches an api to convert the value from one currency to another.

customer-base-analysis

Customer base analysis is concerned with using the observed past purchase behavior of customers to understand their current and likely future purchase patterns. More specifically, as developed in Schmittlein et al. (1987), customer base analysis uses data on the frequency, timing, and dollar value of each customer's past purchases

exploring-the-world-of-football

FIFA 19 is a football simulation video game developed by EA Vancouver as part of Electronic Arts' FIFA series. Announced on 6 June 2018 for its E3 2018 press conference, it was released on 28 September 2018 for PlayStation 3, PlayStation 4, Xbox 360, Xbox One, Nintendo Switch, and Microsoft Windows.It is the 26th installment in the FIFA series. As with FIFA 18, Cristiano Ronaldo initially as the cover athlete of the regular edition: however, following his unanticipated transfer from Spanish club Real Madrid to Italian side Juventus, new cover art was released, featuring Neymar, Kevin De Bruyne and Paulo Dybala.

laravelproject

Project listing application

laravelupdated.github.io

market-basket-analysis-on-food-items

Frequent Itemsets via Apriori Algorithm Apriori function to extract frequent itemsets for association rule mining We have a dataset of a mall with 7500 transactions of different customers buying different items from the store. We have to find correlations between the different items in the store. so that we can know if a customer is buying apple, banana and mango. what is the next item, The customer would be interested in buying from the store.

mobile-price-comparison-of-amazon-and-flipkart

Web Scraping (also termed Screen Scraping, Web Data Extraction, Web Harvesting etc.) is a technique employed to extract large amounts of data from websites whereby the data is extracted and saved to a local file in your computer or to a database in table (spreadsheet) format.

mounty-backend-test

network-analysis-on-cricketers-to-track-down-best-partnerships

Statistics have always had a significant role in sports. As I mentioned above, sports analytics is on the rise and will continue to play a significant role in how teams operate, pick their players, how they play the game, etc. Cricket is no different. The runs scored by a batsman, the wickets taken by a bowler, or the matches won by a cricket team – these are all examples of the most important numbers in the game of cricket.

phonebook

phonedirectory

prediction-of-tariff-rates

Tariff is a list of expenses that incur while transporting the goods from one distance to another distance. Tariff is also dependent on seasonal and non-seasonal factors also. This project is aimed at predicting the tariff ratesfor truck load by using the different machine learning algorithms like lasso regression, elastic net regression, ridge regression and linear regression. Tariffisa combination of lot ofthings and tariff rate is dependent on some ofthe factorslikeYear, Road, SeasonalImpact, Fuel Cost,Distance, Weight, Toll charge, Demand, labour cost, travel expenses etc. Using some ofthese factors and by employing the above-mentioned machine learning regression algorithms we will be trying to predict the tariff rates on the trucks. By doing this we can help the industriesto estimate the tariffratesso that they can take the necessary actions and they can make their business run inprofitable way. This model helps small- and large-scale firms to control and manage the cost on transport.

r2d2_frontend

random_image_generator

It fetches an api and generates a set of random images.

rms2

shishir349.github.io

smart-irrigation-system

speechapi-examples

A small collection of examples that use the Web Speech API.

speechapiexample

study_like_maniacs

upgrad_project