Chandan A

Responsive expert experienced in monitoring database performance, troubleshooting issues and optimizing database environment. Possesses strong analytical skills, excellent problem-solving abilities, and deep understanding of database technologies and systems. Equally confident working independently and collaboratively as needed and utilizing excellent communication skills.

• Travel Data Analysis (Open Source Project from Kaggle)

In this project travel data was imported from Kaggle by using pandas library. Statistical parameters are determined, Data cleaning is performed and Exploratory data analysis is performed by plotting various visualisation plots such as barplot, piechart etc., Python, Matplotlb, seaborn, Pandas library. https://www.kaggle.com/code/chandanashwath/travel-data-analysis-code 

• IPL Cricket 2022 Data Analysis(Open Source Project)

 In this project IPL matches 2022 data was imported from Kaggle by using pandas library. Statistical parameters are determined, Data cleaning is performed and Exploratory data analysis is performed by plotting various visualization plots such as barplot, piechart etc., Python, Matplotlb, seaborn, Pandas library. IPL_2022_Analysis | Kaggle 

• Mobile Price classification using EDA and ML Model

 In this project Mobile price train data was imported from Kaggle by using Pandas library. Statistical parameters are determined, Data cleaning is performed and Exploratory data analysis is performed by plotting various visualisation plots such as boxplot. corelation heatmap is computed. Performed various machine learning models such as logistic regression, decision tree, SVM, random forest and Naive Bayes. Calculated the accuracy and compared the best among the models. Confusion matrix is computed., Python, Pandas library.

 Mobile_Price_Classification_using_EDA_and_ML_Model | Kaggle 

• Language Prediction using NLP and ML

The language data is imported from Kaggle using Pandas. This data comprises of seventeen languages. Individual language is extracted and data cleaning is performed and statistical parameters are determined. Barplot is plotted to find out data records of individual languages. The given data is splitted into train and test data to build and evaluate the model. Accuracy is calculated using actual and predicted value. Confusion matrix and classification report is computed. Prediction function is used to predict the text language., Python, Pandas.

 Language_Prediction_using_NLP_and_ML | Kaggle