Tag Archives: z score prediction model

Campus Recruitment Prediction with Source Code Python

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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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Predicting Student Performance Using Machine Learning

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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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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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