NLP Based project presentation: Analyzing Automobile Prices

•Télécharger en tant que PPTX, PDF•

0 j'aime•5 vues

Explore the factors influencing automobile prices and how data science is used to predict and analyze these prices. Discover the latest trends in the automotive industry and how pricing strategies are evolving. Join us as we uncover the secrets behind car prices. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights

Données & analyses

By:- Sayed Mohammed Faisal
Automobile Car Prices
Prediction

Research Objective
The aim of my project is to build a model that can
accurately predict the price of car based on various car
features such as model, year, mileage, engine
specification, fuel type etc.

Project Contents
 Importing Libraries & Data Loading
 EDA (Exploratory Data Analysis) & Preprocessing
 Train & Test Split
 Model Building
 Linear Regression
 Decision Tree
 Random Forest
 Support Vector Regression
 XGBoost Regressor
 Hyperparameter Tuning on Random Forest
 All Models Tabled
 Predicted Values By Models
 Conclusion

Importing Libraries & Data Loading
Libraries:-
1. Pandas (pd)
2. Numpy (np)
3. Matplotlib.pyplot (plt)
4. Seaborn (sns)
Installing XGBoost
Loading the data using pd.read_csv

EDA (Exploratory Data Analysis) & Preprocessing

Model Building
Model Selection:-
Choosing appropriate machine learning algorithms for car automobile car price
prediction. Common models includes linear regression, random forests,
decision tree, support vector machines (SVM) and advanced techniques like
gradient boosting and neural networks.
Building the following models
 Linear Regression
 Decision Tree
 Random Forest
 Support Vector Regression
 XGBoost Regressor
 Tuned Random forest using GridSearchCV

Linear Regression
Linear Regression values:-
MAE = 1725.308896, MSE = 5.414754e+06,
RMSE = 2326.962298, R2 = 0.749437

Decision Tree
Decision Tree values:-
MAE = 1088.506268, MSE = 2.574009e+06,
RMSE = 1604.371921, R2 = 0.880890

Random Forest
Random forest values:-
MAE = 889.633243 ,
MSE = 1.624665e+06,
RMSE = 1274.623525,
R2 = 0.924820

Support Vector Regression (SVM)
SVM values:-
MAE = 1735.425563,
MSE = 5.789631e+06,
RMSE = 2406.165250,
R2 = 0.732090

XGBoost Regressor
XGBoost values:-
MAE = 803.072896, MSE = 1.299842e+06,
RMSE = 1140.106075, R2 = 0.939851

Tuned Random Forest
values:-
MAE = 835.592968,
MSE = 1.423818e+06,
RMSE = 1193.238335,
R2 = 0.934114
HYPER PARAMETER TUNING
Tuning Random Forest Regressor using GridSearchCV

Conclusion
All the models mentioned above shows good MAE, MSE, RMSE and
R2 values.
After hyperparameter tuning the random forest model using grid
searchCV the model has improved in as seen by the R2 score.
However, XGBoost regressor which is an advanced ensemble machine
learning algorithm gives the best values considered MAE = 803.072896,
MSE = 1.299842e+06, RMSE = 1140.106075, R2 = 0.939851.

Contenu connexe

Similaire à NLP Based project presentation: Analyzing Automobile Prices

Generative AI In Logistics_Object Automation

Object Automation

ml project ppt.pptx

KarunaDobariya

House Sale Price Prediction

sriram30691

SB_MSC-Apex_Structures_LTR_pt

anandakumara MB

Machine Learning Model for M.S admissions

Omkar Rane

Support vector machines (SVMs) often contain a large number of support vectors which reduce the run-time speeds of decision functions. In addition, this might cause an overfitting effect where the resulting SVM adapts itself to the noise in the training set rather than the true underlying data distribution and will probably fail to correctly classify unseen examples. To obtain more fast and accurate SVMs, many methods have been proposed to prune SVs in trained SVMs. In this paper, we propose a multi-objective genetic algorithm to reduce the complexity of support vector machines as well as to improve generalization accuracy by the reduction of overfitting. Experiments on four benchmark datasets show that the proposed evolutionary approach can effectively reduce the number of support vectors included in the decision functions of SVMs without sacrificing their classification accuracy.

A Multi-Objective Genetic Algorithm for Pruning Support Vector Machines

Mohamed Farouk

From Lose to Profit

Mohammed Awad

Online advertising and large scale model fitting

Wush Wu

CSL0777-L07.pptx

KonkoboUlrichArthur

Discover the fascinating world of data science with this beginner-friendly introduction to the field. This presentation covers key concepts and skills, making it perfect for those who are new to data science or looking to refresh their knowledge. You'll learn about the role of a data scientist, the data science process, and popular tools and techniques used in the industry. Content Overview: Introduction to Data Science Key Components and Terminology Data Types and Sources Data Cleaning and Preprocessing Exploratory Data Analysis Feature Engineering and Selection Machine Learning Algorithms Model Evaluation and Validation Data Visualization Techniques Essential Tools and Libraries Real-World Applications and Use Cases Building a Data Science Career Tags: #DataScience #MachineLearning #BigData #Analytics #DataVisualization #AI #Python #DataEngineering #Statistics

DataScience-101

Karthikeyan VK

Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays. Agenda: Introduction of Xgboost Real World Application Model Specification Parameter Introduction Advanced Features Kaggle Winning Solution

Xgboost

Vivian S. Zhang

IRJET - Stock Market Prediction using Machine Learning Algorithm

IRJET Journal

XGBoost: the algorithm that wins every competition

Jaroslaw Szymczak

Project template for projects looks like this

kaniuppu

Project template for presenting it before the panel

AkshatMehrotra14

By using less than 0.5% of the original training dataset the aggregated out-of sample result, over the period the model was tested, 7 days in May 2017, shows that the average difference between the actual and the forecasted average daily hourly German EPEX power prices differed 0.4%. The presence of outliers, heteroskedastic residuals and sparseness of prices at lower price levels in the training data set can explain that two of the days in the test period differed by more than +/- 10%.

Forecasting day ahead power prices in germany using fixed size least squares ...

Niklas Ignell

Deepak-Computational Advertising-The LinkedIn Way

yingfeng

Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot! If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students. --------------------------------------------------------------- Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout. --------------------------------------------------------------- Speaker Bio: Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays. Pre-requisite(if any): R /Calculus Preparation: A laptop with R installed. Windows users might need to have RTools installed as well. Agenda: Introduction of Xgboost Real World Application Model Specification Parameter Introduction Advanced Features Kaggle Winning Solution Event arrangement: 6:45pm Doors open. Come early to network, grab a beer and settle in. 7:00-9:00pm XgBoost Demo Reference: https://github.com/dmlc/xgboost

Xgboost

Vivian S. Zhang

Data Assessment and Analysis for Model Evaluation

SaravanakumarSekar4

MSE.pptx

JantuRahaman

Similaire à NLP Based project presentation: Analyzing Automobile Prices (20)

Generative AI In Logistics_Object Automation

ml project ppt.pptx

House Sale Price Prediction

SB_MSC-Apex_Structures_LTR_pt

Machine Learning Model for M.S admissions

A Multi-Objective Genetic Algorithm for Pruning Support Vector Machines

From Lose to Profit

Online advertising and large scale model fitting

CSL0777-L07.pptx

DataScience-101

Xgboost

IRJET - Stock Market Prediction using Machine Learning Algorithm

XGBoost: the algorithm that wins every competition

Project template for projects looks like this

Project template for presenting it before the panel

Forecasting day ahead power prices in germany using fixed size least squares ...

Deepak-Computational Advertising-The LinkedIn Way

Xgboost

Data Assessment and Analysis for Model Evaluation

MSE.pptx

Plus de Boston Institute of Analytics

Enhancing Cybersecurity: An In-depth Analysis of Travelblog.org

Boston Institute of Analytics

Description: This presentation offers a deep dive into SQL Injection (SQLi) and Cross-Site Request Forgery (CSRF) vulnerabilities, demonstrating their impact through real-world examples. Join us to learn how to prevent and mitigate these threats, and take the first step towards a career in cybersecurity with our specialized courses at Boston Institute of Analytics. https://bostoninstituteofanalytics.org/cyber-security-and-ethical-hacking/

Exploring Web Security Threats: A Practical Study on SQL Injection and CSRF

Boston Institute of Analytics

This project demonstrates a machine learning approach to detecting credit card fraud using advanced algorithms and techniques. The project utilizes a dataset containing various features such as transaction amount, merchant location, time of transaction, and others to build a predictive model. The presentation covers data preprocessing steps, feature engineering techniques, and the selection of machine learning algorithms such as logistic regression or random forest. It also discusses model evaluation metrics and the importance of fraud detection in financial institutions for safeguarding against fraudulent activities. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Detecting Credit Card Fraud: A Machine Learning Approach

Boston Institute of Analytics

This project showcases an AI-driven approach to detecting credit card fraud using machine learning algorithms. The project utilizes a dataset containing transactions with various features such as transaction amount, location, and time. The goal is to build a predictive model that can accurately identify fraudulent transactions and minimize financial losses for banks and customers. The presentation covers data preprocessing techniques, feature engineering, and the application of machine learning algorithms such as logistic regression or random forests. It also discusses model evaluation metrics and the importance of fraud detection in the banking industry. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Detecting Credit Card Fraud: An AI-driven Approach

Boston Institute of Analytics

This project presents a machine learning approach to predicting house prices using a dataset containing various features such as the size of the house, number of bedrooms, location, and others. The project aims to build a predictive model that can accurately estimate the selling price of a house based on its features. The presentation covers data preprocessing steps, feature selection techniques, and the application of machine learning algorithms such as linear regression or decision trees. It also discusses model evaluation metrics and the potential impact of the model on the real estate industry. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting House Prices: A Machine Learning Approach

Boston Institute of Analytics

This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

This presentation dives into the detailed analysis of vulnerabilities discovered in the web infrastructure of Aladel.net, highlighting potential security risks and offering insights into strengthening the website's defenses. Learn about the methods used to identify these vulnerabilities and the recommended strategies to mitigate them, ensuring a more secure online presence for Aladel.net for more information explore our ethical hacking course : https://bostoninstituteofanalytics.org/cyber-security-and-ethical-hacking/

Decoding Loan Approval with Predictive Modeling in Action Discovering Weaknes...

Boston Institute of Analytics

This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/

HTML Injection Attacks: Impact and Mitigation Strategies

Boston Institute of Analytics

Delve into the world of e-commerce order prediction and discover how data science is revolutionizing inventory management and customer satisfaction. Learn how predictive analytics can forecast future orders, optimize inventory levels, and enhance the overall shopping experience. Join us as we unravel the complexities of e-commerce forecasting. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights

E-Commerce Order PredictionShraddha Kamble.pptx

Boston Institute of Analytics

Delve into the realm of predictive modeling for loan approval. Learn how data science is revolutionizing the lending industry, making the loan approval process faster, more accurate, and fairer. Discover the key factors that influence loan decisions and how predictive modelling is shaping the future of lending. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights

Decoding Loan Approval: Predictive Modeling in Action

Boston Institute of Analytics

Analyzing Movie Reviews : Machine learning project

Boston Institute of Analytics

Explore the latest techniques and technologies used in classifying fetal health, from traditional methods to cutting-edge AI approaches. Understand the importance of accurate classification for prenatal care and fetal well-being. Join us to delve into this critical aspect of healthcare. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights

Data Science Project: Advancements in Fetal Health Classification

Boston Institute of Analytics

This presentation, delivered by a Boston Institute of Analytics student, explores the critical world of credit card fraud detection. Join us as we delve into the various methods and technologies employed to identify and prevent fraudulent activity, safeguarding both consumers and financial institutions. The presentation covers: The ever-evolving landscape of credit card fraud Techniques for analyzing transaction data Machine learning's role in fraud detection Strategies for preventing fraudulent transactions This session is ideal for anyone interested in data analytics, cybersecurity, or the financial services industry. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights

Combating Fraudulent Transactions: A Deep Dive into Credit Card Fraud Detection

Boston Institute of Analytics

This presentation by Harsh Pakhare from the Boston Institute of Analytics explores the potential of machine learning to predict liver disease in Indian patients. The presentation dives into the factors specific to the Indian population that can influence liver health, along with the analysis of relevant patient data. It showcases how machine learning algorithms can be used to identify patients at risk and potentially improve early detection. This presentation is ideal for: Data Science enthusiasts interested in healthcare applications Individuals seeking insights into liver disease in India Anyone curious about the potential of machine learning in medical prediction visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights

Predicting Liver Disease in India: A Machine Learning Approach

Boston Institute of Analytics

Explore the cutting-edge presentations by students from the Boston Institute of Analytics as they delve into the realm of employee churn prediction. Gain valuable insights into the methodologies, techniques, and predictive models used to anticipate and mitigate employee turnover. Dive into real-world case studies and discover how businesses can leverage predictive analytics to retain top talent and foster organizational success. Enroll in our comprehensive course to master the skills needed for predictive analytics in the workplace. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights

Employee Churn Prediction: Artificial Intelligence Project Presentation

Boston Institute of Analytics

Explore how data science can be used to predict employee churn using this data science project presentation, allowing organizations to proactively address retention issues. This student presentation from Boston Institute of Analytics showcases the methodology, insights, and implications of predicting employee turnover. visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more data science insights

Predicting Employee Churn: A Data-Driven Approach Project Presentation

Boston Institute of Analytics

Explore the power of Natural Language Processing (NLP) and Data Science in uncovering valuable insights from Flipkart product reviews. This presentation delves into the methodology, tools, and techniques used to analyze customer sentiments, identify trends, and extract actionable intelligence from a vast sea of textual data. From understanding customer preferences to improving product offerings, discover how NLP Data Science is revolutionizing the way businesses leverage consumer feedback on Flipkart. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx

Boston Institute of Analytics

This NLP Project Presentation explores how Natural Language Processing (NLP) and Data Science are revolutionizing the prediction of heart disease. Discover how cutting-edge techniques are being used to analyze textual data, such as patient records and medical reports, to predict the likelihood of heart disease with unprecedented accuracy. For more details on data science Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...

Boston Institute of Analytics

This data science project presentation provides a detailed overview of how data science can be leveraged to predict salary trends. We explore the methodologies, tools, and datasets used in salary prediction models, along with real-world examples and case studies. From analyzing factors influencing salary to building predictive models, this presentation covers the entire process, offering insights for recruiters, job seekers, and data enthusiasts alike. For more details on data science. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Salary Using Data Science: A Comprehensive Analysis.pdf

Boston Institute of Analytics

Dive into the cutting-edge realm of predictive analytics with our PowerPoint presentation on loan default prediction. Explore key methodologies, data insights, and predictive models that empower informed decision-making in the lending industry. Gain invaluable insights to mitigate risks and enhance profitability. Visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more

Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT

Boston Institute of Analytics

Plus de Boston Institute of Analytics (20)