Tag Archives: pandas

Iris Flower Classification with Decision Trees Web App

Posted on by

Objective:

To build a web application that can accurately classify Iris flower species based on their sepal and petal characteristics using a Decision Tree machine learning algorithm.

Dataset: The Iris flower dataset, which contains 150 samples of Iris flowers, each with measurements for sepal length, sepal width, petal length, and petal width. The dataset is labeled with the species of each flower: Iris setosa, Iris versicolor, and Iris virginica.

Methodology:

  1. Data Preprocessing: Load the dataset and split it into training and testing sets. Perform feature scaling to normalize the data.
  2. Decision Tree Model Building: Train a decision tree model on the training data using scikit-learn library. Tune the hyperparameters of the model to obtain the best performance.
  3. Web App Development: Use Flask web framework to create a web app that allows users to input the sepal and petal measurements of an Iris flower and displays the predicted species using the trained decision tree model.
  4. Model Interpretation: Interpret the decision tree to gain insights into which features are most important in classifying the Iris flower species.

Tools and Technologies:

  1. Python
  2. scikit-learn
  3. Flask
  4. HTML
  5. CSS
  6. pandas
  7. numpy
  8. matplotlib.

Conclusion:

Decision Trees are a simple yet powerful machine learning algorithm for classification tasks. In this project, we have built a decision tree model to classify Iris flower species with high accuracy and developed a web application that allows users to interactively predict the species of an Iris flower based on its sepal and petal measurements. The web app can be used for real-world applications such as plant identification, environmental monitoring, and plant breeding.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.6.10, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Download

Detecting Fraudulent Transactions using Random Forest Project Proposal

Posted on by

Project Title: Detecting Fraudulent Transactions using Random Forest

Project Description: The objective of this project is to develop a machine learning model using Random Forest to detect fraudulent transactions. Fraudulent transactions can cause significant financial losses to organizations, and machine learning models can help identify such transactions in real-time.

As a student, you can start by collecting a dataset of transactions that includes both legitimate and fraudulent transactions. You can then preprocess the data, perform exploratory data analysis, and engineer relevant features that may help the model identify fraudulent transactions.

You can then use Random Forest, an ensemble learning method that combines multiple decision trees, to build a model that can learn the patterns of fraudulent transactions. You can train the model on the labeled dataset and evaluate its performance using metrics such as accuracy, precision, recall, and F1 score.

Once the model is trained and tested, you can deploy it in a real-time environment using web technologies such as Flask or Django. The model can be integrated into an application that can monitor transactions and flag any that are deemed suspicious.

The final deliverable can be a report that details the methodology, findings, and recommendations for the field of application.

Expected Deliverables:

  1. A detailed analysis of the transaction dataset
  2. A machine learning model using Random Forest to detect fraudulent transactions
  3. An evaluation of the model's performance using metrics such as accuracy, precision, recall, and F1 score
  4. A web application that can flag fraudulent transactions in real-time
  5. A comprehensive report that details the methodology, findings, and recommendations for the field of application.

Tools and Technologies:

  1. Python
  2. Scikit-learn
  3. Pandas
  4. NumPy
  5. Flask or Django

Project Timeline: As a student project, the timeline can be flexible and depend on your availability. However, you can follow this timeline:

  1. Week 1: Understanding fraud detection and transaction datasets
  2. Week 2-3: Data Collection and Preprocessing
  3. Week 4-5: Model Development and Training
  4. Week 6-7: Model Evaluation and Deployment
  5. Week 8: Report Writing and Presentation.

Anomaly Detection in Time Series Data using Autoencoder Project Proposal

Posted on by

Project Title: Anomaly Detection in Time Series Data using Autoencoder

Project Description: The objective of this project is to detect anomalies in time series data using Autoencoder, a type of deep neural network that can learn to encode and decode input data. Anomaly detection in time series data is important in various fields, such as finance, manufacturing, and healthcare, as it can help identify unusual patterns or events that may require further investigation.

As a student, you can start by understanding the concept of time series data and anomalies. You can then collect a dataset of time series data, such as sensor readings, stock prices, or healthcare data. The data should have both normal and abnormal instances.

You can preprocess the data, split it into training and testing sets, and use Autoencoder to build a model that can learn the normal behavior of the data. Once the model is trained, you can use it to predict the output of the testing set. Any instance that deviates significantly from the predicted output can be considered an anomaly.

You can evaluate the performance of the model using metrics such as precision, recall, and F1 score. You can also visualize the anomalies to understand their patterns and characteristics.

The final deliverable can be a report detailing the methodology, findings, and recommendations for the field of application.

Expected Deliverables:

  1. A detailed analysis of time series data and anomalies
  2. A deep learning model using Autoencoder to detect anomalies
  3. An evaluation of the model's performance using metrics such as precision, recall, and F1 score
  4. A visualization of the anomalies to understand their patterns and characteristics
  5. A comprehensive report that details the methodology, findings, and recommendations for the field of application.

Tools and Technologies:

  1. Python
  2. TensorFlow or Keras
  3. Pandas
  4. NumPy
  5. Matplotlib or Seaborn

Project Timeline: As a student project, the timeline can be flexible and depend on your availability. However, you can follow this timeline:

  1. Week 1: Understanding time series data and anomalies
  2. Week 2-3: Data Collection and Preprocessing
  3. Week 4-5: Model Development and Training
  4. Week 6-7: Model Evaluation and Visualization of Anomalies Week 8: Report Writing and Presentation.

Hypo Thyroid Disease prediction Machine Learning Project

Posted on by

Hypo Thyroid Disease prediction Machine Learning Project

Subscribe YouTube For Latest Update Click Here

Latest Machine Learning Project with Source Code

Buy Now ₹1501

Hypothyroid diseases (underactive thyroid) is a condition in which the body doesn't produce enough of important thyroid hormones. The condition may lead to various symptoms at late ages. More information about the disease is available at https://www.mayoclinic.org/diseases-conditions/hypothyroidism/symptoms-causes/syc-20350284 .

The Data

The data was from: http://archive.ics.uci.edu/ml/datasets/thyroid+disease. I used "allhypo.data" for the analysis. "allhypo.names" contains the column names of the data. Include the info about primary data processing in the Jupyter notebook list below.

set of algorithms performed to carry out the analysis of the "thyroid-disease" database published in the UCI page
URL data source
data: https://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/sick-euthyroid.data
names: https://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/sick-euthyroid.names

Algorithms

  • Naıve Bayes
  • KNN
  • ANN
  • Random Forest
  • SVM
  • FSF
  • PCA
  • LCA

Related sources

Ionita, Irina. (2016). Prediction of Thyroid Disease Using Data Mining Techniques. BRAIN. Broad Research in Artificial Intelligence and Neuroscience. Vol.7. pp.115-124.
URL: https://www.researchgate.net/publication/321145710_Prediction_of_Thyroid_Disease_Using_Data_Mining_Techniques

Ammulu K., Venugopal. (2017). Thyroid Data Prediction using Data Classification Algorithm. IJIRST –International Journal for Innovative Research in Science & Technology. Vol.4. Issue 2. July 2017. ISSN (online): 2349-6010
URL: http://www.ijirst.org/articles/IJIRSTV4I2054.pdf

Geetha K., Santosh S. Eficient Thyroid Disease Classification Using Differential Evolution with SVM. Journal of Theoretical and Applied Information Technology. Vol.88. No.3. E-ISSN: 1817-3195
URL: http://www.jatit.org/volumes/Vol88No3/4Vol88No3.pdf

Banu, Gulmohamed. (2016). Predicting Thyroid Disease using Linear Discriminant Analysis (LDA) Data Mining Technique. Communications on Applied Electronics. 4. 4-6. 10.5120/cae2016651990. URL: https://www.caeaccess.org/research/volume4/number1/banu-2016-cae-651990.pdf

Lou H, Wang L, Duan D, Yang C,Mammadov M (2018) RDE: A novel approach to improve the classification performance and expressivity of KDB. PLoS ONE 13(7): e0199822. URL: https://doi.org/10.1371/journal.pone.0199822

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.

Crime Data Analysis Project in Machine Learning

Posted on by

Subscribe YouTube For Latest Update Click Here

Latest Machine Learning Project with Source Code

Buy Now ₹1501

Buy Now Project Report ₹1001

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

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.6.10, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.6.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

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.

Movie Recommendation System Project Using Collaborative Filtering, Python Django, Machine Learning

Posted on by

Subscribe YouTube For Latest Update Click Here

Latest Machine Learning Project with Source Code

Buy Now ₹2501

( 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.

Project Features :-

  1. User can register and login.
  2. User can search through various movies and look through its details.
  3. User can give rating to the movies.
  4. User can add movie to their watch list.
  5. User can get movie recommendation (Recommendation algorithm (Collaborative Filtering) which suggests new movies based on the ratings given by user.)

Algorithm :

Collabortive Filtering (Recommender Algorithm)

  1. Collaborative filtering filters information by using the interactions and data collected by the system from other users. It's based on the idea that people who agreed in their evaluation of certain items are likely to agree again in the future.

  2. When we want to find a new movie to watch we'll often ask our friends for recommendations. Naturally, we have greater trust in the recommendations from friends who share tastes similar to our own.

  3. Collaborative-filtering systems focus on the relationship between users and items. The similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items.

  4. There are two types of collaborative filtering

    1. User-based, which measures the similarity between target users and other users.
    2. Item-based, which measures the similarity between the items that target users rate or interact with and other items.

    I have used user based collaborative filtering in this project.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy

Database

SQLite

Requirements
python 3.7

pip3

virtualenv

Installation

pip install -r requirements.txt --user

Run server locally

$ python manage.py runserver

Go to localhost:8000

  1. Admin email - admin@admin.com
  2. admin pass - admin

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.