1. PROJECT NAME: STOCK PREDICTION SYSTEM
Duration : 3 months
Language Used : Python
Libraries Used : Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch,Matplotlib,Seaborn, Statsmodels, Keras.
Client : Hyderabad
Project Description:
The Stock Prediction System is a data science project that employs machine learning algorithms to forecast stock market performance. By analyzing historical stock data and relevant financial indicators, the system generates predictions to assist investors in making informed decisions. Through data preprocessing, feature engineering, and training various machine learning models, the system aims to provide reliable insights and identify potential investment opportunities. It is important to note that stock market prediction is challenging and subject to market volatility, so the system’s predictions should be used as a tool for analysis and decision support rather than relying solely on them for trading activities.
2. PROJECT NAME: BITCOIN PREDICTION SYSTEM
Duration : 3 months
Language Used : Python
Libraries Used : Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch,Matplotlib,Seaborn,,Statsmodels,Keras,Prophet,XGBoost ,Cryptory
Client : Gurgaon
Project Description:
The Bitcoin Prediction System is a data science project that leverages machine learning techniques to forecast the future price movement of Bitcoin, the popular cryptocurrency. By analyzing historical Bitcoin price data, market indicators, and relevant factors, the system aims to generate predictions and insights for traders and investors. Through data preprocessing, feature engineering, and training machine learning models, the system seeks to provide valuable information to assist in decision-making regarding Bitcoin investments. However, it is essential to note that cryptocurrency markets are highly volatile, and accurate predictions can be challenging. Thus, the system’s predictions should be used as a tool for analysis and decision support rather than relied upon solely for trading activities.
3.PROJECT NAME: ACCURACY , PRECISION PREDICTION ON WOMEN HEART DISEASES USING MACHINE
Duration : 3 months
Language Used : Python
Libraries Used : Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Statsmodels, XGBoost, LightGBM, CatBoost, TensorFlow,Keras’
Client : Calicut
Project Description:
The Accuracy and Precision Prediction on Women Heart Diseases Using Machine project aims to develop a data science model that can accurately predict the presence of heart diseases in women. By analyzing various health-related features, such as age, blood pressure, cholesterol levels, and other medical indicators, the model uses machine learning algorithms to classify individuals as either having or not having heart diseases. The project focuses on achieving high accuracy and precision in the predictions to ensure reliable identification of potential heart disease cases in women. This project can contribute to early detection, prevention, and personalized treatment plans for women at risk of heart diseases, improving overall healthcare outcomes.
4. PROJECT NAME: CREDIT CARD FRAUD DETECTION SYSTEM USING MACHING LEARNING
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn, Imbalanced-learn, XGBoost, LightGBM, CatBoost.
Client : Pune
Project Description:
The Credit Card Fraud Detection System is a data science project that utilizes machine learning techniques to identify and prevent fraudulent credit card transactions. By analyzing transactional data, including various features such as transaction amount, location, and customer behavior patterns, the system aims to detect abnormal and potentially fraudulent activities. Through data preprocessing, feature engineering, and training machine learning models, the system seeks to accurately classify transactions as either genuine or fraudulent. This project helps financial institutions and credit card companies to proactively identify fraudulent activities, protect their customers, and minimize financial losses due to fraudulent transactions.
5. PROJECT NAME: BUILD A CHATBOTS
Duration : 3 months
Language Used : Python
Libraries Used : NLTK, Spacy, Gensim, Scikit-learn, TensorFlow, Keras, PyTorch, ChatterBot, Rasa, Dialogflow, Microsoft Bot Framework, Facebook Messenger API, Twilio API, Flask, Django.
Client : Ahmedabad
Project Description:
The Build a Chatbots data science project involves creating an intelligent conversational agent that can interact with users in a natural language format. Using natural language processing (NLP) techniques and machine learning algorithms, the project focuses on training the chatbot to understand user inputs, generate appropriate responses, and simulate human-like conversations. The project typically involves preprocessing text data, building language models, implementing dialogue management systems, and integrating with chat platforms or applications. The goal is to develop a chatbot that can effectively understand user queries, provide accurate information, and engage in meaningful conversations, enhancing user experience and automating customer support or other interactive tasks.
6.RECOMMENDATION SYSTEM
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, NumPy, Scikit-learn, TensorFlow, Keras
Client : Bangalore
Project Description
The Recommendation System project focuses on developing algorithms that suggest relevant items to users based on their preferences and behavior. The project typically involves processing and analyzing large datasets, implementing collaborative filtering or content-based filtering techniques, and evaluating the performance of the recommendation system. By leveraging machine learning and data mining techniques, the goal is to create personalized recommendations that enhance user experience and improve engagement. The project can be applied to various domains such as e-commerce, streaming services, social media platforms, and more.
7.SENTIMENT ANALYSIS
Duration : 4 months
Language Used : Python
Libraries Used : NLTK, Scikit-learn, TextBlob, VaderSentiment
Client : Hyderabad
Project Description :
Sentiment Analysis aims to determine the sentiment or emotional tone behind a piece of text. The project involves preprocessing text data, applying machine learning or deep learning algorithms, and classifying the sentiment as positive, negative, or neutral. By analyzing social media posts, customer reviews, or any textual data, sentiment analysis provides valuable insights into public opinion, customer feedback, and brand reputation. The project can be useful for market research, social media monitoring, customer support, and sentiment-driven decision making.
8.COLOR DETECTION WITH PYTHON
Duration : 4 months
Language Used : Python
Libraries Used : OpenCV, NumPy, Matplotlib
Client : Kozhikode
Project Description :
The Color Detection project involves detecting and recognizing colors in images or video streams. By leveraging computer vision techniques, the project aims to extract color information from pixels, segment objects based on color ranges, and visualize the results. The project typically involves image processing, color space conversions, thresholding, and contour detection. Color detection can have applications in various fields, including image editing, object tracking, industrial automation, and fashion or design industries.
9.FAKE NEWS DETECTION
Duration : 4 months
Language Used : Python
Libraries Used : NLTK, Scikit-learn, TensorFlow, Keras
Client : Wayanad
Project Description :
Fake News Detection focuses on developing models to identify and classify fake or misleading news articles. The project involves preprocessing textual data, extracting relevant features, and training machine learning or deep learning models. By analyzing the linguistic patterns, source credibility, and contextual information, the goal is to build an accurate classifier that can distinguish between real and fake news. The project contributes to combating misinformation, promoting media literacy, and ensuring the dissemination of reliable information.
10.FOREST FIRE PREDICTION
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, NumPy, Scikit-learn, TensorFlow, Keras
Client : Trivandrum
Project Description :
The Forest Fire Prediction project aims to develop models that can predict the occurrence and severity of forest fires. By analyzing various environmental factors such as temperature, humidity, wind speed, and vegetation, the project involves preprocessing and analyzing large datasets, implementing machine learning algorithms, and evaluating the predictive performance. Accurate forest fire prediction can aid in early detection, prevention, and effective firefighting strategies, contributing to environmental conservation and public safety.
11.ROAD LANE LINE DETECTION
Duration : 4 months
Language Used : Python
Libraries Used : OpenCV, NumPy, Matplotlib
Client : Vishakapatnam
Project Description :
Road Lane Line Detection focuses on detecting and tracking lane lines on roads using computer vision techniques. By processing video frames or images, the project aims to extract lane lines, estimate their positions, and visualize the results. The project typically involves image preprocessing, edge detection, Hough transforms, and curve fitting. Road lane line detection plays a vital role in autonomous vehicles, driver assistance systems, and road safety applications.
12.DRIVER DROWSINESS DETECTION
Duration : 4 months
Language Used : Python
Libraries Used : OpenCV, Dlib, TensorFlow, Keras
Client : Chennai
Project Description :
The Driver Drowsiness Detection project aims to develop a system that can detect signs of driver fatigue or drowsiness in real-time. By analyzing facial landmarks, eye movements, and head pose, the project involves image and video processing, machine learning, and computer vision techniques. The goal is to build an effective drowsiness detection system that can alert drivers and reduce the risk of accidents caused by drowsy driving.
13.GENDER PREDICTION USING SOUND
Duration : 4 months
Language Used : Python
Libraries Used : Librosa, Scikit-learn, TensorFlow, Keras
Client : Jaipur
Project Description :
Gender Prediction using Sound focuses on predicting the gender of a speaker based on their voice. The project involves extracting relevant acoustic features from audio signals, preprocessing the data, and training machine learning or deep learning models. By analyzing pitch, frequency, and other audio characteristics, the goal is to build an accurate gender classification system. The project finds applications in speech recognition, voice assistants, and forensic analysis.
14.FACEBOOK AI’S DETECTION TRANSFORMER (DETR)
Duration : 4 months
Language Used : Python
Libraries Used : PyTorch, torchvision, PIL, NumPy
Client : Hoobli
Project Description :
Facebook AI’s DEtection TRansformer (DETR) project focuses on object detection and tracking in images. The project utilizes a transformer-based architecture, combining object detection and recognition into a single framework. It involves preprocessing image data, training the model using annotated datasets, and evaluating its performance. DETR provides a flexible and efficient approach to object detection, eliminating the need for complex handcrafted components like region proposal networks. The project has applications in computer vision, autonomous driving, and surveillance systems.
15.REAL-TIME IMAGE ANIMATION
Duration : 4 months
Language Used : Python
Libraries Used : OpenCV, NumPy, TensorFlow, Keras
Client : Pune
Project Description :
Real-Time Image Animation involves creating interactive systems that can animate images in real-time. The project utilizes deep learning techniques, such as generative adversarial networks (GANs) or convolutional neural networks (CNNs), to synthesize and manipulate images based on user inputs or predefined models. By combining computer vision and deep learning, the goal is to create immersive and engaging visual experiences. Real-Time Image Animation finds applications in gaming, augmented reality, virtual reality, and creative arts.
16.RECREATING JOHN SNOW’S GHOST MAP
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, Matplotlib, Geopandas, Folium
Client : Lucknow
Project Description :
Recreating John Snow’s Ghost Map project aims to recreate the famous map created by Dr. John Snow during the 1854 cholera outbreak in London. The project involves analyzing historical data, geospatial visualization, and storytelling. By leveraging data visualization libraries, the project visualizes the cholera cases and their relationship with water pumps, showcasing the significance of spatial analysis in public health. Recreating John Snow’s Ghost Map project contributes to understanding the importance of data-driven decision making and the impact of data visualization in epidemiology.
17.A NEW ERA OF DATA ANALYSIS IN BASEBALL
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
Client : Indore
Project Description :
A New Era of Data Analysis in Baseball project focuses on applying data science techniques to analyze and gain insights from baseball data. The project involves preprocessing and analyzing large datasets, performing statistical analysis, and building predictive models. By leveraging machine learning algorithms, the goal is to extract valuable information about player performance, team strategies, and game outcomes. A New Era of Data Analysis in Baseball has revolutionized the way baseball teams make decisions, improve player performance, and gain a competitive edge.
18.SPEECH EMOTION RECOGNITION
Duration : 4 months
Language Used : Python
Libraries Used : librosa, NumPy, Scikit-learn, TensorFlow, Keras
Client : Ludhiana
Project Description:
Speech Emotion Recognition aims to classify the emotional state of a speaker based on their speech. The project involves preprocessing audio data, extracting relevant features such as pitch, intensity, and spectral characteristics, and training machine learning or deep learning models. By analyzing acoustic properties and linguistic cues, the goal is to accurately recognize emotions like happiness, sadness, anger, or surprise. Speech Emotion Recognition has applications in voice assistants, customer feedback analysis, and mental health monitoring.
19.COMPARING COSMETICS BY INGREDIENTS
Duration : 4 months
Language Used : Python
Libraries Used : BeautifulSoup, requests, Pandas, NumPy, Matplotlib
Client : Agra
Project Description:
Comparing Cosmetics by Ingredients project focuses on analyzing and comparing cosmetic products based on their ingredients. The project involves web scraping to collect product information, preprocessing and cleaning the data, and performing ingredient analysis. By comparing the composition of different products, the project aims to provide insights into ingredient similarities, product categorization, and potential allergens. Comparing Cosmetics by Ingredients can aid consumers in making informed choices, assist regulatory bodies, and promote transparency in the cosmetic industry.
20.UBER DATA ANALYSIS IN R
Duration : 4 months
Language Used : Python,R
Libraries Used : dplyr, ggplot2, leaflet, lubridate
Client : Meerut
Project Description :
Uber Data Analysis in R involves analyzing and visualizing Uber ride data using the R programming language. The project utilizes data manipulation and visualization libraries to explore patterns, trends, and insights from the Uber dataset. It includes tasks like data cleaning, aggregation, geographical visualization, and time series analysis. Uber Data Analysis in R helps uncover ride patterns, peak hours, popular locations, and other valuable information that can inform business strategies, optimize operations, and improve user experience.
21.DIABETIC RETINOPATHY DETECTION
Duration : 4 months
Language Used : Python
Libraries Used : TensorFlow, Keras, OpenCV, NumPy
Client : Raipur
Project Description :
Diabetic Retinopathy Detection aims to detect and classify the severity of diabetic retinopathy, a common complication of diabetes that affects the eyes. The project involves preprocessing and analyzing retinal images, training deep learning models, and predicting the presence and severity of retinopathy. By leveraging image classification algorithms and computer vision techniques, the goal is to provide early detection and aid in the diagnosis of diabetic retinopathy, potentially preventing vision loss and improving patient care.
22.TRAFFIC SIGNS RECOGNITION
Duration : 4 months
Language Used : Python
Libraries Used : TensorFlow, Keras, OpenCV, NumPy
Client : Jabalpur
Project Description :
Traffic Signs Recognition focuses on developing models that can accurately detect and classify traffic signs from images or video streams. The project involves preprocessing image data, training deep learning models, and evaluating their performance. By leveraging convolutional neural networks (CNNs), the goal is to build a robust system that can aid in traffic sign detection, autonomous driving, and road safety. Traffic Signs Recognition contributes to intelligent transportation systems and enhances driver assistance technologies.
23.IMAGE CAPTION GENERATOR PROJECT IN PYTHON
Duration : 4 months
Language Used : Python
Libraries Used : TensorFlow, Keras, NLTK, NumPy
Client : Gorakhpur
Project Description :
The Image Caption Generator project aims to generate textual descriptions or captions for images. The project involves preprocessing image data, training deep learning models such as encoder-decoder architectures, and generating captions based on image content. By combining computer vision and natural language processing techniques, the goal is to build a system that can understand and describe the visual content of images. Image Caption Generator has applications in image indexing, content generation, and accessibility for visually impaired individuals.
24.CUSTOMER SEGMENTATION
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
Client : Jalandar
Project Description :
Customer Segmentation focuses on dividing a customer base into groups based on their shared characteristics, behaviors, or preferences. The project involves analyzing customer data, applying clustering algorithms, and visualizing the segments. By identifying distinct customer segments, businesses can tailor marketing strategies, personalize offerings, and enhance customer satisfaction. Customer Segmentation is widely used in customer relationship management, market research, and targeted advertising.
25.ASL RECOGNITION WITH DEEP LEARNING
Duration : 4 months
Language Used : Python
Libraries Used : TensorFlow, Keras, OpenCV, NumPy
Client : Bikaner
Project Description :
ASL Recognition with Deep Learning aims to develop models that can recognize American Sign Language (ASL) gestures from video input. The project involves preprocessing video data, training deep learning models, and predicting the corresponding ASL gestures. By leveraging convolutional neural networks (CNNs) and sequence modeling, the goal is to build an accurate ASL recognition system that can aid communication with the hearing-impaired community and facilitate inclusivity.
26.GENDER BIAS IN GRADUATE ADMISSIONS
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
Client : Noida
Project Description :
Gender Bias in Graduate Admissions project focuses on analyzing and uncovering potential gender biases in the graduate admissions process. The project involves preprocessing and analyzing admission data, performing statistical analysis, and visualizing the findings. By evaluating admission rates, patterns, and trends, the goal is to identify any disparities or biases that may exist and raise awareness for fair and equitable admissions practices.
27.EXTRACT STOCK SENTIMENT FROM NEWS HEADLINES
Duration : 4 months
Language Used : Python
Libraries Used : NLTK, Scikit-learn, TensorFlow, Keras
Client : Chennai
Project Description :
Extract Stock Sentiment from News Headlines aims to analyze news headlines and predict the sentiment impact on stock prices. The project involves preprocessing textual data, training machine learning or deep learning models, and predicting the sentiment associated with stock market news. By correlating news sentiment with stock price movements, the project provides insights into market trends, sentiment-driven trading strategies, and sentiment analysis in financial markets.
28.CLUSTERING BUSTABIT GAMBLING BEHAVIOR
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, NumPy, Scikit-learn, Matplotlib
Client : Kanpur
Project Description :
Clustering Bustabit Gambling Behavior focuses on clustering and analyzing user behavior patterns in the Bustabit gambling platform. The project involves preprocessing and analyzing user data, applying clustering algorithms, and visualizing the results. By grouping users based on their gambling behavior, the project aims to uncover player segments, identify high-risk profiles, and provide insights into player preferences and strategies.
29.THE IMPACT OF CLIMATE CHANGE ON BIRDS
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
Client : Ajmer
Project Description :
The Impact of Climate Change on Birds project aims to analyze the impact of climate change on bird populations. The project involves preprocessing and analyzing bird observation data, performing statistical analysis, and visualizing the results. By examining bird migration patterns, breeding behaviors, and species distribution, the goal is to understand the ecological consequences of climate change on bird populations and guide conservation efforts.
30.KIDNEY STONES AND SIMPSON’S PARADOX
Duration : 4 months
Language Used : Python
Libraries Used : Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
Client : Kanpur
Project Description :
Kidney Stones and Simpson’s Paradox project focuses on analyzing medical data to explore Simpson’s Paradox in the context of kidney stone treatments. The project involves preprocessing and analyzing patient data, performing statistical analysis, and visualizing the findings. By investigating treatment outcomes and potential confounding variables, the goal is to illustrate the counterintuitive nature of Simpson’s Paradox and highlight the importance of considering confounders in data analysis and decision-making processes.
