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Loan Defaulter Prediction Machine Learning Projects

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Using supervised machine learning to train a model with credit default data to determine the probability and/or classification (“default” vs “non-default”) of the user’s liability. The UI will take user input such as, such as education level, sex, marital status, payment history and income, and will return a classification.

An app like this would be useful for financial and lending institutions to understand and manage the risk of their loans and lending portfolios.

 

Goals/Outcome

  • Determining probability of user liability
  • Creating an interactive UI that will take users input and return an output
  • To determine if a neural network vs logistic regression is the better model for classification

Models Created

  • Logistic Regression
  • Random Forest Model
  • Deep Neural Network

About

Probability of Credit Card Default, Machine Learning

Technologies Used : -

  • beautifulsoup4==4.6.0
  • certifi==2018.4.16
  • chardet==3.0.4
  • click==6.7
  • Flask==1.0
  • gunicorn==19.8.0
  • idna==2.6
  • itsdangerous==0.24
  • Jinja2==2.10
  • MarkupSafe==1.0
  • numpy==1.14.3
  • pandas==0.22.0
  • python-dateutil==2.7.2
  • pytz==2018.4
  • requests==2.18.4
  • scikit-learn==0.19.1
  • scipy==1.0.1
  • six==1.11.0
  • SQLAlchemy==1.2.7
  • urllib3==1.22
  • Werkzeug==0.14.1

 

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Fake News Detection using Machine Learning Natural Language Processing

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Fake News Detection using Machine Learning Natural Language Processing . A NLP and Machine Learning based web application used for detecting fake news. Uses NLP for preprocessing the input text. Uses XGBoost model for predicting whether the input news is Fake or Real.

here are tons of stories articles, where the news is fake or cooked up. With numerous advances in tongue Processing and machine learning, we will actually build an ml model which is in a position to detect if a bit of stories ... Here we'll be using artificial neural network models to verify the genuinity of the article.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Database

SQLite

Dataset Link: https://www.kaggle.com/c/fake-news/data

Training Model File 

Fake_News_Classifier_Using_LSTM.ipynb

Fake_News_Classifier_using_Machine_Learning.ipynb

Output Generated File

xgb_fake_news_predictor.pkl

 

 

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Crime Data Analysis Project in Machine Learning

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Crime Data Analysis Project in Machine Learning .Crime analyses is one among the important application of knowledge mining. data processing contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications It can help the analysts to spot crimes faster and help to form faster decisions.
The main objective of crime analysis is to seek out the meaningful information from great deal of knowledge and disseminates this information to officers and investigators within the field to help in their efforts to apprehend criminals and suppress criminal activity. In this project, Kmeans Clustering is employed for crime data analysis.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python

Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn

Database

SQLite

Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.
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Movie Recommendation System Project Using Collaborative Filtering, Python Django, Machine Learning

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( Note : Project Included with Complete Source code Database Plus Documentation, Synopsis, Report)

Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming.

A Web Base user-item Movie Recommendation Engine using Collaborative Filtering By matrix factorizations algorithm and thus the advice supported the underlying concept is that if two persons both liked certian common movies,then the films that one person has liked that the opposite person has not yet watched are often recommended to him.

A recommender system is a type of information recommend movies to user according to their area of interest. Our recommender system provide personalized information by learning the user‟s interests from previous interactions with that user[2]. In pattern recognition, the knearest neighbours algorithm (k-NN) is a flexible method used for classification. In following cases, the input consists of the k closest examples in given space. If k = 1, then the object is simply assigned to the class of that single nearest neighbour.

Algorithms Implemented 

  • Content based filtering
  • Collaborative Filtering
    • Memory based collaborative filtering
      • User-Item Filtering
      • Item-Item Filtering
    • Model based collaborative filtering
      • Single Value Decomposition(SVD)
      • SVD++
  • Hybrid Model
    • Content Based + SVD

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy

Database

SQLite

Requirements
python 3.6

pip3

virtualenv

Read Before Purchase  :

  1. One Time Free Installation Support.
  2. Terms and Conditions on this page: https://projectworlds/terms
  3. We offer Paid Customization installation Support
  4.  If you have any questions please contact  Support Section
  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.