2d-image-to-3d-model

The Goal of this project is to convert the 2-dimensional colour image into 3-dimensional model using the Open3d library. Depth Estimation is used to convert the 2-dimensional colour image to Point Clouds and then convert it into 3d Mesh using open3d.

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

ai_coding_test

Specialized in ML or DL

bird-species_classification-project

break-the-ice-with-python

The repository is about 100+ python programming exercise problem discussed, explained, and solved in different ways

cotton-disease-prediction

data-extraction-from-pdf

datasets

datasets-mlops

decision-tree-classification

Classification Problem of Decision Tree

diffusion-model-tutorial

How diffusion models work

employee-retention-dashboard

The Employee Retention Dashboard is been made on Tableau data visualization Tool. It gives the complete information about the Employee Retention datasets, based upon the features present in it. Using the important features we can prevent the retention, which will help the organizations growth.

exploratory-data-analysis

Exploratory data analysis for deeper understanding of data  Goal: Perform analysis on dataset of different cars with their specifications. • Before performing exploratory data analysis (EDA), data profiling was performed. • Dataset was checked up for missing values and treated with automate way to fill in the missing values using Imputer library. • After treatment of missing values, univariate, bivariate and multivariate charts and plots for deeper understanding of data.

farzad-r.github.io

handwritten-digit-project

job-a-thon_nov-2021-

Approach Problem Statement: You are working as a data scientist with HR Department of a large insurance company focused on sales team attrition. Insurance sales teams help insurance companies generate new business by contacting potential customers and selling one or more types of insurance. The department generally sees high attrition and thus staffing becomes a crucial aspect. To aid staffing, you are provided with the monthly information for a segment of employees for 2016 and 2017 and tasked to predict whether a current employee will be leaving the organization in the upcoming two quarters (01 Jan 2018 - 01 July 2018) or not, given: 1.Demographics of the employee (city, age, gender etc.) 2.Tenure information (joining date, Last Date) 3.Historical data regarding the performance of the employee (Quarterly rating, Monthly business acquired, designation, salary) As the objective was to predict if an employee will leave the organization in the upcoming two quarters, the target variable was taken such that if an employee leaves the organization within 180 days of review it was taken was 1 and 0 otherwise i.e., if the last working day is 25-11-2017 and a review was conducted on 01-05-2017(208 days prior), target would be 0 and for the next review conducted on 01-06-2017(177 days prior), the target would be 1. The training data was taken only till 01-08-2017 as a full 180 days was required for prediction. The predictions had to be done at review level for each employee otherwise there would not be sufficient data and the changes in employee performance /behaviour might be difficult to catch if data was minimized to one row per employee. Data Pre-Processing/Feature Engineering: In the dataset, there are 13 features which are Emp_ID, Reporting Date, Age, Gender,City,Education,Salary,DateofJoining,LastWorkingDate,Joining_Designation, Designation, Total_Business_Value, Quarterly_Rating. First step in Building a Model is to understand the Data-Set, and after understanding I came to know that, there are ‘2200’ Duplicate values present in the ‘Emp_ID’ column (primary key). After that I’d Drop all the Duplicate values on the basis of last ‘Reporting Date’, and we get the Distinct ‘Emp_ID’ column. The Next step is that the target variable is not specifically mentioned in the train data. For constructing the target variable as shown in the definition, one should first look at the ‘LastWorkingDate’ column. Wherever the column has null values, that means the employee is continuing his/her work at the organization at least in the next year. Wherever any date record is appearing, that means the employee has left the organization on that particular date. So as per definition, we will put 0 where ‘LastWorkingDate’ column is null and 1 where ‘LastWorkingDate’ column has a date. Next, we take the age of that employee the last it was reported. Gender and City were taken from the dataset given. Education and Salary were also taken the last time it was reported. Joining Designation is taken as it is from the dataset. Designation is the designation of the employee at the last time it was reported. Total Business Value is the sum of the Total Business Value acquired by the employee. Quarterly_Rating is the rating the employee was given the last time it was reported. Model Building: Now, before building the model, the categorical feature ‘Gender’, ‘Education-Level’, ‘City’, ‘Quarterly Rating’ was One-hot encoded. All the numerical features were scaled using StandardScalar. Then search for the parameter values like ‘n-estimators’ and ‘max-depth’ which gives the best f1-score using GridSearchCV. Model Selection: Before finalizing on Decision Tree; few classification models like LogisticRegression, KNN, SVM, XGBoost and GradientBoost were also applied on the dataset. XGBoost led to overfitting the data. SVM, Gradient Boost and Random Forest performed well on the data. Since Decision Tree gave a good f1-score = 0.6966, this model was selected to predict the employee attrition.

k-means-cluster-on-mall-customers

Goal:Understand about customers coming up in mall. Performed steps of data profiling, data preprocessing and exploratory data analysis on the dataset. Used K-Means clustering and Hierarchical clustering for clusters formation.

lane-detection-using-mask-r-cnn

Aim: To detect the Road LAnes lines using Mask R-CNN

lane-detection-using-open-cv

Aim: To detect Road lanes line using Open-CV

language-translation

learnopencv

Learn OpenCV : C++ and Python Examples

lgmvip-datascience

LetsGrowMore Data Science Intern

llm-zero-to-hundred

This repository contains different LLM chatbot projects (RAG, LLM agents, etc.) and well-known techniques for training and fine tuning LLMs.

llm_calculator

loan-approval-prediction

Classification Problem as Loan Status of an employee is found using categorical variable (Y,N) STEPS PERFORMED: • Import Dataset • Data Profiling •Data Pre-processing •Exploratory Data Analysis •Split Data into training & testing set and create models(Compare Accuracy) •Compare predicted and achieved results MODELS USED: Logistic Regression Decision Tree Classification Random Forest K-Nearest Neighbours

mask-detection-using-yolov4

Aim: To detect if person wearing mask or not using Yolov4 Object detection model.

motherboard-defect

multiple-linear-regression

Machine Learning

multiple-linear-regression.

nlp-basic-natural-language-processing-

Natural Language Processing (NLP) MODELS Present: 1.Tokenization 2.Stemming 3.Lemmatization 4.Bag of Words 5.TF-IDF (Term Frequency- Inverse Document Frequency) 6.Word2Vec