Apni Dukaan

Last Updated on May 3, 2021

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• Developing an Ecommerce Website for a local vendor for selling clothes having two panels for admin and users.

• With user viewing and buying the clothes through a payment gateway and admin able to update the wardrobe section through the custom django admin panel.

• Will be using REST API for cross platform communication.

• Technologies: Django REST framework , React, Bootstrap and Braintree for payment gateway.

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Salary Predictor

Last Updated on May 3, 2021

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This is a web app created using open source python library called Streamlit. This library is mainly used to create web apps for machine learning and data science. In this Project I collected data required from the

Kaggle. I Used Sklearn library to get the model required for the data and I fitted the data using in-built methods in it. So I created a web app which contain two pages named Home and Prediction. In home page I displayed the data collected and a scatter graph plotted using the matplotlib library with the help of data collected from Kaggle. In prediction page there will be a text filed where we can enter the experience of the employee and click the button which ultimately shows the precited salary for that employee. Stream lit Web app gives the output of a local host URL. So we have to deploy it globally. So I deployed the web app in Heroku platform. Here in this project I just downloaded a small data set to test how it works. So here a large data set can also be taken but the process will be different in training the model. For large datasets the data should be split to train and testing data so that we can train the model accurately and advanced algorithms to train the model is also used. So based on our convenience and requirements we can do machine learning models and save it into a file and this file can be used while creating a web app.

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Logisticregression

Last Updated on May 3, 2021

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Problem Statement :

  • X Education sells online courses to industry professionals. The company markets its courses on several websites and search engines like Google.
  • Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals.
  • Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.

Business Goal:

  • X Education needs help in selecting the most promising leads, i.e. the leads that are most likely to convert into paying customers.
  • The company needs a model wherein you a lead score is assigned to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance.
  • The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.

Strategy

  • Source the data for analysis
  • Clean and prepare the data
  • Exploratory Data Analysis.
  • Feature Scaling ? Splitting the data into Test and Train dataset.
  • Building a logistic Regression model and calculate Lead Score.
  • Evaluating the model by using different metrics - Specificity and Sensitivity or Precision and Recall.
  • Applying the best model in Test data based on the Sensitivity and Specificity Metrics.
  • Solution: 
  • Designed logistic Regression model and calculate the Lead Score 


Key Achievement: 

  • Predicted the leads with a accuracy of 80% and found Important features responsible for good conversion rate or the ones' which contributes more towards the probability of a lead getting converted.
  • Prepared a power point presentation with great visualization for clients and Managers.
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Web Base Application Heart Failure Prediction System

Last Updated on May 3, 2021

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In this situation, approximately 17 million people kill globally per year in the whole world because of cardiovascular disease, and they mainly exhibit myocardial-exhibit myocardial infarction and heart failure. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.

In this heart prediction problem statement, we are trying to predict whether the patient's heart muscle pumps blood properly or not using Logistic Regression. In this project, a dataset is downloaded from the UCI repository and this dataset is real. this dataset is collected from one of the most famous hospitals is in the United Kingdom (UK) in 2015 and there are 299 patient records and 12 features(attribute) and one label. Based on that 12 features, we will predict whether the patient's heart working properly or not.

In this problem statement, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning & classifiers to both predict the patient’s survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone.

For model building we use various library packages like Pandas, Scikit learns (sklearn), matplotlib, Seaborn, Tensorflow, Keras, etc., then we will use data description, Data description involves carrying out initial analysis on the data to understand more about the data, its source, volume, attributes, and relationships. Once these details are documented, any shortcomings if noted should be informed to relevant personnel. after that, we use the data cleaning method for cleaning the dataset to check if there are any missing values or not and we split the dataset into training & testing purposes with 70%, 30% criteria. Then the next step is Model Building, The process of model building is also known as training the model using data and features from our dataset. A combination of data (features) and Machine Learning algorithms together give us a model that tries to generalize on the training data and give necessary results in the form of insights and/or predictions. Generally, various algorithms are used to try out multiple modeling approaches on the same data to solve the same problem to get the best model that performs and gives outputs that are the closest to the business success criteria. Key things to keep track of here are the models created, model parameters being used, and their results. And the last step is to analyze the result in this step we check our model score or accuracy by using Confusion Matrix and Model Score. For this model, we got 80% accuracy. In the future, we try to improve that accuracy. For model deployment, we use the python flask and based on that we build the web-based application.


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Hyderabad House Price Predictor

Last Updated on May 3, 2021

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Hyderabad House Price Predictor


ML model which predicts the price of a house based on features like total Sq. ft area,total number of bedrooms,balconies etc.

The front-end of this model is made by boot-strap and Flask,where as the backend is a Machine learning model which is trained on the housing-price dataset and the algorithm used is Random-Forest

the model is hosted at------> https://homepricepredictor.herokuapp.com/



General Overview of the Project 


Starting of with the home page which is designed using bootstrap classes,here we in this template the general overview of the project is mentioned,along with that the parameters which are required for predicting the price of the house are also mentioned here,here's a glimpse of it