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vuyyuru, india
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Ingressou em agosto 25, 2020
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Madhulika T.

@cmadhulika7

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Certified Data Analyst

Data science analyst with, 3 years of experience in Statistical Modeling, Data Mining, Data Visualization and Machine Learning. Quick to grasp new ideas, concepts, to develop innovative and create solutions to problems. Able to work well on my own initiative and can demonstrate the high levels of motivation required to meet the deadlines.
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Portfólio

7842460
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
7842460
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.
Outlier Analysis: Outliers - An outlier is an observation point that is distant from other observations. These data points could be a result of errors or wrongly recorded observations. Logistic Regression: 1`The logistic regression model is performed with some assumptions: that data has no outliers, there are two classes to be predicted, and that no two independent variables are highly correlated to each other. By choosing the best hyper parameters, a test set accuracy of ~80% was obtained. In terms of accuracy, gradient boosting is almost near to random forest. Choosing the best Classifier: As our dataset is imbalanced, only accuracy cannot judge the performance of the model. A plot of the precision vs. recall curves is illustrated below. Appendix – figures and graphs: 1.Distribution graph of age and count plot of default variable 2.Count plot of education separated by default variable and box plot of age variable. 3.Correlation heatmap
The loan default dataset.

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Experiência

Data Analyst

Smartried Software Technologies
dez. 2017 - Atual
Hands on experience in R programming, Python, Minitab. • Data Quality Management for Analytical processes under senior Data Scientists. • Experience in entire data science life cycle, i.e., source=>clean=>explore=>communicate. • Expertise in data exploration techniques using various statistical tools. • Proficient in Data extraction, Data cleaning, Data Loading, Data Transformation, Predictive Modeling using R, Python (Pandas, NumPy, matplotlib, Seaborn) and Data visualization using Tableau

Educação

MBA

Jawaharlal Nehru Technological University, India 2013 - 2015
(2 anos)

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