Bank_Customer_Churn_PredictionsLast Updated on May 3, 2021
This is a classification Machine Learning Model in which I predicted whether the customer will churn or not. In this project, I started with EDA and after performing the EDA, I created dummies for the categorical values. In the dataset there were no missing values and the outliers are few in number and upon carefull analysis of the data, the outliers seem to be genuine entries, so the outliers were not removed. As the data was heavily imbalanced with respect to the target variable, so I performed the UpSampling of the dataset using the SMOTE method. After that, Principal Component Analysis is done to reduce the number of features from 35 to 2 as almost 100% of the variance was explained by these 4 features. After that, a Random Forest Classifier model was trained on the dataset and after that, I performed the Hyperparameter tuning of the model. But it turns out that Hyperparameter optimization did not have any impact on the accuracy of the model and the confusion matrix of both the optimized and unoptimized model was the same.
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Resume Up-LoaderLast Updated on May 3, 2021
Ever you apply to an organisation with cv through mail but it might happen that specific organisation don't know that actually candidate need like job preference or type of job, so it get easier when we use this app called resume up-loader.
It is my first self project using Django (python
framework) called Resume Up-loader .
where you put every detail about yourself ,job location photos,signature,CV,after submitting the information load on the server and next page you can look all your information and download the Resume also ,i am continuously working on it and upgrading that it list all the company on that preference job location for your current qualification and skill it help the candidate to know in which company is he/she is suitable for and it also company to know their candidate batter
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To make this single page website I have use the python web framework called Django
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.
I have also use HTML to define the structure of front-end and use style tag to make this beautiful
Book Recommendation SystemLast Updated on May 3, 2021
Book Recommendation System
Book recommendation is created and deployed in this approach of work, which helps in recommending books. Recommendation achieved by the users feedbacks and rating, this is the online which analyse the ratings, comments and reviews of user, negative positive nature of comments using opinion mining. User searching for the interested book will be displayed in top list and also can read feedback given by people about the book or any searched items. Whenever user search for any book from the large data available, he gets confused from the number of displayed item, which one to choose. In that case recommendation helps and displays on the interested items. This is the trustworthy approach, which is used in this project where selection is based on the dataset.
This project used clustering as the central idea. A clustering approach is used. Clustering is based on similarity where similar elements are kept in a single group. Likewise similar element, the irrelevant elements are also reside in a group, which is another group, based on similarity value or maximum size of cluster. The clustering approach which is used in our work is K-mean clustering for grouping of similar users. It is the unsupervised and simplest learning algorithm, which simplifies mining work by grouping similar elements forming cluster. This is done using a parameter called K-centroids. Distance between each element is calculated for checking the similarity and forming a single cluster to reside the similar elements, after comparing with K-centroid parameter.
In this project, 6 clusters were made.
The project is made with 2 separate datsets in .csv format taken from Kaggle.
- Books dataset
This project is GUI based. The output page has 2 options:
- Rate books
- Recommend books
The user can chose either according to themselves.
In this option, the user can rate books.
In this option the books are recommended to the user, according to their previous readings.
A Review On Weather Forecasting Techniques Using Machine LearningLast Updated on May 3, 2021
Weather depicts the atmospheric conditions of a particular place at a particular time. The basic weather elements comprise of temperature, wind, pressure, cloudiness and humidity. Every day, the Meteorological Department prepares weather maps for the upcoming day with the help of the data obtained from various weather stations around the world. Weather forecasts help in taking measures in advance in case of the probability of bad weather and in planning your day ahead.
Different instruments are used to measure various weather elements like, a thermometer is used to measure the temperature, whereas, a barometer is used to measure pressure. Similarly, a wind vane is used to find the direction of wind and a rain gauge is used to measure the amount of rainfall. Thus, with the help of the data collected through these instruments we get the weather forecast in the form of weather charts.
In order to decrease so much manual labour, these weather forecasting techniques are now getting replaced with machine learning models that can predict future weather quite accurately with the help of previously collected data. In this report, we are discussing some of the weather forecasting techniques that are most-likely to be used in order to get accurate weather predictions result. Herein we are comparing the results of the various models, just to get the best results.
Keywords: Weather Forecasting, ARIMA, Holt Linear, Holt Winter, Stationarity, Dickey- Fuller
Fantasy Cricket GameLast Updated on May 3, 2021
It is an online game where you create a virtual team of real cricket players and score points depending on how your chosen players perform in real life matches. To win a tournament, you must try and get the maximum points and the No. 1 rank amongst other participants. Here's how a Fantasy Cricket game may look like.
1 Opening screen of the application. You can see the players of each category by selecting the category. To begin with, the selection is disabled until a new team is created from the Manage Teams menu. A pop up asking the name of the team appears.
2 The toolbar menu options which allow you to create a new team, open an existing team, save your team and finally evaluate the score of a saved team.
3 After clicking create Team, the left box is populated with player names. As you select a different category, the corresponding list of players is displayed.
4 On double-clicking each player name, the right box gets populated. Points available and used are displayed accordingly.
5 Message if the game logic is not followed
6 Pop-up on clicking Evaluate Score. You can select your team here and the match for which the players' performance is compared.
7 The final score for your fantasy team based on the match selected.
False Alarm Detection SystemLast Updated on May 3, 2021
This project was made for a chemical industry which had sensors installed in various parts of the factory to detect H2S gas which is hazardous to health. Every time one or multiple sensors detected the H2S leak, an emergency alarm rings to alert the workers. For every alarm, the industry calls a team which sanitizes the place and checks for the leak and this was a big cost to the company.
A few of the alarms that ring are not even hazardous. The company gave us the data for each alarm with a final column stating the alarm was dangerous or not.
Unwanted substance deposition (0/1)
The data was first pre-processed and analysis libraries like Numpy and Pandas were used to make it ready to be utilized by a machine learning algorithm.
Problems like standard scaling, categorical data and missing values were handled with appropriate techniques.
Then, we used Logistic Regression model to make a classifier with first five column as independent columns and dangerous column as dependent/target column.
Now whenever, there is a leakage and the alarm rings, the data is sent to us and we predict if it is dangerous or not. If found dangerous then only the team is called to sanitize the place and fix the leak. This saved a lot of money for the company.