- Completed stepwise EDA (Exploratory Data Analysis) and visualization to get data insight & to know important features also their correlation with car price
- Done Feature Engineering includes Features extraction & Features construction based on my domian knowledge and visualization
- Train ML models with multiples regression algorithms then Analysed & compare performance of differents models based of accuracy and complexity
- After comparing, got well accuracy by RandomForestRegressor(cross validation--around 90%)
- Finally Build Web App using streamlit and deploy the model
- Technical tools or library used --
Python,Numpy,Pandas,sklearn,matplotllib,seaborn,streamlit
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- View On Kaggle 💝 - View On Github 💝
- Web App 💝
- I have made ML model which will predict either new customer will accept offer deposit or not?
- Completed stepwise EDA (Exploratory Data Analysis) then visualizatiion to get some idea about important features or correlation
- Done Feature Engineering which includes features extraction & features construction based on domain knowledge and visualization followed by label encoding
- Train ML models with multiples algorithms then Analysed & compare performance of differents models based of accuracy and complexity
- after comparing with all, got well accuracy by RandomForest and XG boost
- Finally after cross validation XG boost won (accuarcy of 85% and recall score 88%)
- Build Pipeline for deployment session
- Technical tools or library used --
Python,Numpy,Pandas,sklearn,matplotllib,XG boost
- EDA (Exploratory Data Analysis) and visualization to get data insight or important features
- Train model with multiples classification algorithms
- Analysed & compare performance of differents models based on F1 score
- SVM and KNN had given best accuracy
- Vary K value still accuracy was almost same 97.2 then after cross validation SVM accuracy was more than KNN
- Finally Build web application using streamlit and deploy the model.
- Web App👉 https://karanchinch10-streamlit-iris-app-0k57bb.streamlitapp.com// 💝
- Technical tools or library used --
Python,Numpy,Pandas,sklearn,matplotllib,html,css,streamlit
- Perform EDA, Data cleaning and Data correction
- Done details visualization on gplay store apps to get basic information or data insight and that will be helpful for decision making like
- Total No of apps of all category (like games,sports,medical,education,beauty..etc) to understand whcih category has highest apps
- Which category has highest demand, rating, installation & reviews
- Total percentages of free and paid apps available in glapy store
- Which category of apps are most installed or like to user
- Average price of paid apps of each category and their demands
- Is there is any relation of apps rating and reviews with insatllation?
- Technical tools or library used --
Python,Numpy,Pandas,sklearn,matplotllib,seaborn,html,css
- I have made this flask project of bank management web application system
- Front end was created by HTML and CSS without use of bootstrap
- Project is specially designed for customer/bank holder to get all basic bank services
- First customer has to open their bank account by filling basic bank details such as Name, Password, DOB, mob no, Initial Deposit and register their bank account
- Once account has been created then they can login with their user ID and password
- User can view and modify their personal details from profile section
- User can withdraw, credit money into their account also can check current balance
- Minimum credit and withdraw should be RS 200 and RS50 and minimum initial deposit should be min 1000 RS while opening account
- User can also change their password by clicking on forgot password and set new password by confirming their name and email ID
- User can Login and Logout their account anytime
- Technical tools or library used --
Python,Flask,MySql Database,XAMPP,html,CSS
- I have made Personal web Portfolio to showcase my skills, technical knowledge and personal projects
- 👉Click to View My Personal Web Porfolio 💝