Campus Recruitment Prediction with Source Code Python

This project aims to predict the salary of students in campus recruitment using a dataset named train.csv. The dataset contains the following columns: sl_no, gender, ssc_p, ssc_b, hsc_p, hsc_b, degree_p, degree_t, workex, etest_p, specialisation, mba_p, status, and salary.

Table of Contents

  1. Introduction
  2. Project Structure
  3. Data Processing and Modeling
  4. Flask Web Application

Introduction

In this project, we analyze the provided dataset and build a predictive model for campus recruitment. We first perform data processing and exploratory data analysis (EDA) using a Jupyter Notebook (notebook.ipynb). Next, we implement a Flask web application (app.py) to deploy the trained predictive model and allow users to make predictions based on the provided input.

Project Structure

  1. train.csv: Dataset containing recruitment-related information.
  2. notebook.ipynb: Jupyter Notebook containing data preprocessing, EDA, and model selection.
  3. app.py: Flask web application for model deployment.
  4. templates/: Directory containing HTML templates for the web application.
    1. index.html: Homepage of the web application.
    2. prediction.html: Page displaying predictions.
  5. requirements.txt: File listing all the necessary libraries for running the web app.
  6. model.pkl: Pickled file containing the trained predictive model (Ridge model).
  7. scaler.pkl: Pickled file containing the scaler used for standardization.

Data Processing and Modeling

In the Jupyter Notebook (notebook.ipynb), we perform the following steps:

  1. Import necessary libraries.
  2. Load the dataset (train.csv).
  3. Preprocess the data by dropping unnecessary columns and handling missing values.
  4. Visualize data through various plots and charts.
  5. Perform one-hot encoding for categorical variables.
  6. Split the dataset into training and testing sets.
  7. Standardize the data using StandardScaler.
  8. Explore and select the best scoring model using GridSearchCV and ShuffleSplit.
  9. Save the best-fitted model and scaler using pickle (model.pkl and scaler.pkl).

Flask Web Application

The Flask web application (app.py) is created to deploy the trained predictive model. It allows users to input their information and receive predictions regarding their placement status and expected salary. The web application consists of two main HTML templates:

  • index.html: The homepage where users input their details.
  • prediction.html: The page displaying the predicted placement status and salary.

To run the web application, use the libraries specified in requirements.txt.

Python runtime : 3.11

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Ai Mental Health Chatbot project python with source code

This is an AI-powered bot designed to provide emotional support and assistance to individuals struggling with mental health issues. It can help individuals access mental health resources, offer guidance and support. With the integration of Language translation, this chatbot will be very efficient as it will be able to break the language barriers.

The creation of a chatbot capable of language translation, holds transformative potential, acting as a catalyst in overcoming language barriers for effective communication and information exchange. Its impact spans diverse sectors, including: healthcare, commerce, and governance etc. offering a versatile solution to bridge linguistic gaps.

https://codeaxe.co.ke/multilingobot/

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 3.8, 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 3.8
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

AI Healthcare chatbot project python with source code

This is a Python-based project for dealing with human symptoms and predicting their possible outcomes. The primary goal of this project is to forecast the disease so that patients can get the desired output according to their primary symptoms.

The Healthcare AI Chatbot is an innovative technology solution designed to provide patients with easy access to medical advice and care. The chatbot utilizes artificial intelligence algorithms to identify and diagnose symptoms, provide basic medical advice, and direct patients to appropriate healthcare services. The goal of this project is to create an intelligent and user-friendly chatbot that can assist patients in identifying their symptoms, provide medical advice, and help them access healthcare services, including telemedicine consultations.

The Healthcare AI Chatbot will be designed to be accessible to anyone with a smartphone or computer. Patients will be able to interact with the chatbot via a web-based or mobile-based interface, allowing them to ask questions, describe their symptoms, and receive medical advice. The chatbot will use natural language processing algorithms to understand the patient's questions and provide appropriate responses.

Technologies Used:

Natural Language Processing (NLP): NLP is a branch of artificial intelligence that enables computers to understand and interpret human language. This technology can be used in developing an AI chatbot that can understand patient queries, provide appropriate responses, and direct patients to appropriate healthcare services.
Machine Learning (ML): ML is a type of AI that enables computers to learn and improve from experience without being explicitly programmed. ML algorithms can be trained on medical data to enable the chatbot to diagnose medical conditions and provide appropriate medical advice.

Big Data Analytics: Big data analytics can be used to analyze large datasets of medical information, including symptoms, diagnoses, and treatments. This data can be used to train the chatbot's algorithms and improve its accuracy and effectiveness.

User Interface Design: User interface design is an important aspect of developing an AI chatbot that is easy to use and understand. Designing an intuitive and user-friendly interface can help patients interact with the chatbot more effectively and obtain the medical advice and care they need.

Tech Used :

  1. Tkinter
  2. Spacy
  3. Huggingface
  4. NLP

Installation

Use the package manager pip to install the requirements.txt file package.

  • pip install -r requirements.txt

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Weapon Detection System Using CNN FLask Web app

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ML powered system for detecting weapons within images

Business Problem

  1. Mass shootings have become increasingly prevalent at public gatherings
  • Creating an algorithm that that be integrated into traditional surveillance systems can be used to detect threats faster and more efficiently than those monitored by people
  • In modern surveillance systems, there is a person or group of people, in charge of watching monitors which can span across multiple floors of a given area
  1. Violence on social media platforms such as Youtube, Facebook, and TikTok
  • An algorithm that integrate itself into traditional upload systems can detect violent videos before they are spread on a given website
  • Considering the graphs below, the United States ranks among the top 5 countries in terms of firearm deaths

Solution

  1. Create a neural network that can be integrated into traditional surveillance systems
  2. This neural network will be able to detect whether a firearm is present in a frame, and if so, it will notify authorities/managers of its detection

Requirements

  1. keras (PlaidML backend --> GPU: RX 580 8GB)
  2. numpy
  3. pandas
  4. opencv (opencv-contrib-python)
  5. matplotlib
  6. beautifulsoup

Datasets

Predicting Student Performance Using Machine Learning

In today's educational landscape, understanding the factors that contribute to a student's academic performance is crucial for educators, parents, and policymakers. This project leverages machine learning techniques to predict a student's performance in mathematics based on various factors. By providing accurate predictions, this tool can help identify students who may need additional support and tailor educational strategies accordingly.

Note: This Project is for Educational Purposes Only

The Student Exam Performance Predictor project is developed for educational purposes to showcase the application of machine learning techniques in predicting student performance. The results obtained from this project are based on a specific dataset and machine learning model, and should not be considered as definitive or accurate predictions for real-world scenarios. The primary goal of this project is to demonstrate the end-to-end process of developing a machine learning model and provide insights into the factors influencing student performance.

This project aims to predict student performance based on various factors such as gender, ethnicity, parental level of education, lunch type, test preparation course, and exam scores. The machine learning model trained on a dataset of student information can provide insights into predicting a student's performance in mathematics.

Features

  1. Predicts student performance in mathematics based on multiple factors.
  2. Provides insights into the influence of gender, ethnicity, parental level of education, lunch type, and test preparation course on student performance.
  3. User-friendly interface for inputting student information and obtaining predictions.

Dataset

The dataset used for training the machine learning model is sourced from Kaggle - Students Performance in Exams. It contains information about students' demographics, parental education, lunch type, test preparation course, and their corresponding math scores.

Model Training

The machine learning model is trained using a supervised learning algorithm, such as a decision tree or random forest, to predict the math score based on the input features. The dataset is split into training and testing sets to evaluate the model's performance.

Technology Used

  1. Python
  2. Machine Learning
  3. Pandas
  4. Numpy
  5. Scikit-learn
  6. Flask
  7. HTML
  8. CSS

Installation Step : -

  1. Python 3.7.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

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Employee Attrition Prediction using machine learning

Attrition is the silent killer that can switly disable even the most successful and stable of the organizations in a shockingly spare amount of time. Hiring new employees are extremely complex task that requires capital, time and skills.Also new employee costs a lot more than that Persons salary.

  • The cost of hiring an employee goes far beyond just paying for their salary to encompass recruiting, training, benefits, and more.
  • Small companies spent, on average, more than $1,500 on training, per employee, in 2019.
  • Integrating a new employee into the organization can also require time and expenditures.
  • It can take up to six months or more for a company to break even on its investment in a new hire.

The Cost of Hiring a New Employee - Investopedia

In this project, I have developed a Machine Learning Model to predict the Employee Attrition by implementing various Machine Learning Algorithms. Conducted exploratory data analysis using various data visualization techniques.

Achieved good accuracy on the 'IBM HR Analytics Employee Attrition & Performance' dataset from Kaggle,using Logistic Regression.

Algorithm :

  1. *Logistic Regression* is used for development of model.

Technology Used

  1. Python
  2. Machine Learning
  3. Pandas
  4. Numpy
  5. Scikit-learn
  6. Flask
  7. HTML
  8. CSS

Installation Step : -

  1. Python 3.7.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

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Chronic kidney disease prediction machine learning web app

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Buy Now Project Report ₹1001

This webapp was developed using Flask Web Framework. The models used to predict the diseases were trained on large Datasets. All the links for datasets and the python notebooks used for model creation are mentioned below in this readme. The webapp can predict following Disease. Our kidneys perform an important function to help filter blood and pass waste as urine. Chronic kidney disease, also called chronic kidney failure, describes the gradual loss of this function. At advanced stages, dangerous levels of fluid, electrolytes and wastes can build up in the body. Once this happens, patients must go through dialysis or consider a transplant. Our goal in this project is to see if we can predict if a patient will have chronic kidney disease or not using 24 predictors. If we are able to find variables with a strong influence on kidney failure, we may be able to detect and help patients at risk to prevent it.

 Algorithm :

  1. *Random Forest Classifier* is used for development of model.
  2. Only three algorithms are used to predict the output. They are *Logistic Regression*, *XGBoost* and *Random Forest*.\
    1. Accuracy of the model using Logistic Regression is 95%.
    2. Accuracy of the model using Random Forest Classifier is 99%.
    3. Accuracy of the model using XGBoost Classifier is 99%.

Technology Used

  1. Python
  2. Machine Learning
  3. Pandas
  4. Numpy
  5. Scikit-learn
  6. Flask
  7. HTML
  8. CSS

Installation Step : -

  1. Python 3.7.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

Brain Stroke Prediction Machine Learning Source Code

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Brain Stroke Prediction Machine Learning. Stroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests.

Datasets 

This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.

Link - https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset

Attribute Information

1) id: unique identifier
2) gender: "Male", "Female" or "Other"
3) age: age of the patient
4) hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension
5) heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease
6) ever_married: "No" or "Yes"
7) work_type: "children", "Govt_jov", "Never_worked", "Private" or "Self-employed"
8) Residence_type: "Rural" or "Urban"
9) avg_glucose_level: average glucose level in blood
10) bmi: body mass index
11) smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"*
12) stroke: 1 if the patient had a stroke or 0 if not
*Note: "Unknown" in smoking_status means that the information is unavailable for this patient

Technology used

  1. Python
  2. Machine Learning
  3. Pandas
  4. Numpy
  5. Scikit-learn
  6. Flask
  7. HTML
  8. CSS

Installation Step : -

  1. python 3.7
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python app.py

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  6. You can download the product after the purchase by a direct link on this page.

Vehicle number plate detection Deep Learning Project Source code

Number Plate recognition, also called License Plate realization or recognition using image processing methods is a potential research area in smart cities and the Internet of Things. An exponential increase in the number of vehicles necessitates the use of automated systems to maintain vehicle information for various purposes.

Project Descriptions :-

  1.  This is **"SSD"** algorithm based **Tensorflow Object Detection** model.
  2. It can detect the number plates of vehicle.
  3. For text extraction **"EasyOcr"** model is used
  4.  Based on the number plates it will give corresponding state (from India) of that vehicle.
  5. This is flask based webapp which you can deploy it on pivotal cloud.
  6. For accurate results Image size should be minimum of **800 x 600**.
  7. Supported image file formats are **".PNG"**,**".JPG"**,**".JPEG"**.

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

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Age and Gender Detection Using Deep Learning Python Flask

The "Age and Gender Detection Using Deep Learning" Flask project aims to build a web application that can accurately detect the age and gender of a person from an input image. The project leverages deep learning techniques to analyze facial features and make predictions. The web application will provide an intuitive user interface where users can upload images and get real-time predictions for age and gender.

Key Features:

  1. Image Upload: The web application allows users to upload images containing human faces for analysis.
  2. Age Detection: The deep learning model will predict the age of the person in the uploaded image. The model is trained on a large dataset of facial images with corresponding age labels.
  3. Gender Detection: The model will also predict the gender of the person in the uploaded image as either male or female.
  4. Real-Time Prediction: The system provides real-time predictions and displays the age and gender results immediately after image upload.
  5. User-Friendly Interface: The Flask web application offers a user-friendly interface that is easy to navigate and interact with.

Technical Details:

  1. Deep Learning Model: The age and gender detection models are built using deep learning frameworks like TensorFlow or PyTorch. The age model is usually a regression model, while the gender model is a binary classification model.
  2. Convolutional Neural Network (CNN): The models are likely based on CNN architectures to effectively learn facial features and patterns for age and gender prediction.
  3. Flask Web Framework: The web application is developed using the Flask framework, which is a lightweight and easy-to-use Python web framework.
  4. HTML/CSS and JavaScript: The front-end of the web application is built using HTML/CSS for layout and design, while JavaScript may be used for dynamic elements and handling image uploads.
  5. Deployment: The application may be deployed on a web server using platforms like Heroku, AWS, or Microsoft Azure, making it accessible online.

Limitations:

  1. Accuracy: The accuracy of age and gender prediction depends on the quality and diversity of the training data. The model may not always provide precise predictions, especially for images with challenging angles, lighting, or occlusions.
  2. Face Detection: The system assumes that the input image contains only one face, and face detection is not a part of this project.
  3. Age Range: The model's predictions might be limited to a specific age range, and its accuracy might decrease for age groups outside the training data.

Conclusion:

The "Age and Gender Detection Using Deep Learning" Flask project is an exciting application that demonstrates the capabilities of deep learning in analyzing facial features for age and gender prediction. The real-time web interface enhances user experience, making it easy for users to explore the system's predictions. However, the project also acknowledges its limitations in terms of accuracy and the need for proper data representation. With further improvements and advancements in deep learning and dataset diversity, the system's performance could be enhanced in the future.

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

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