Ninad Mandavkar's Projects
Contains end to end Data Analysis portfolio project made using Excel, SQL, Tableau & Power BI
Created a QnA chatbot that generate answers based on the questions asked pertaining to the uploaded data file.
Contains solved Advanced Excel problems
Contains solved MySQL problems
Contains solved Power BI questions
Contains solved Tableau questions and their visuals
Content: Structure of ANN, Creating, compiling & fitting ANN model, plotting the metrics (loss, validation loss, accuracy & validation accuracy), Influence of Early stopping on ANN model
Content: Structure of CNN, Convolutional layer, Pooling layer, Fully connected layer, Dense layer, output, Image classification, Creating, compiling and training the model on epochs, testing the model on gradio
Content: NLP introduction, Components of NLP, Steps to build NLP pipeline, Bag of Words (BoW) model, Term Frequency Inverse Document Frequency (TFIDF) model
Content: RASA framework, Creating a RASA assistant, comprehending Data (nlu, stories, rules), domain & config files, understanding important CLI commands in RASA
Content: Null value check, 1st & 2nd level text cleaning, pipelining Tfidf & Logistic Regression
Contains end to end Data Analysis portfolio project made using Excel, SQL, Tableau & Power BI
Content: Cloning a repository, checking the status, pushing the repository on GitHub
Created a web application that can classify any forest/sea/building image from the internet into it's correct category.
Content: Root node, Decision node & Leaf nodes, Attribute Selection Measure (ASM), Feature Importance (Information Gain), Gini index
Content: Classification, Sigmoid function, Decision Boundary, Cost function, Gradient descent, Overfitting, Regularisation
Content: Multivariate regression, Feature scaling, Polynomial regression, gradient descent, regression using sklearn
Content: Linear Regression, Cost function, Influence of w & b on Cost function, gradient descent, global & local minimum
Content: Unsupervised ML, Agglomerative & Divisive Hierarchical clustering, EDA using Dendrogram, Customer clustering
Content: Unsupervised ML, Clustering, Customer Segmentation, WCSS, elbow method
Content: Machine Learning, KNN concept, Euclidean distance, Data preprocessing, Scaling the data, Performing train-test split, Applying KNeighbors Classifier, Predicting Y_pred based on X_test, Evaluation using Confusion Matrix, Accuracy score, Recall value & Precision, Underfitting & Overfitting, Measures to overcome Underfitting & Overfitting
Content: Machine Learning (Overview & Process Flow), Linear Regression concept, Assumption checking, Linearity, Multi co-linearity, Auto correlation, Outliers, Null value treatment, EDA, Splitting the test and train data, Applying LinearRegression on train data, Predicting y_pred based on test data, Constructing a dataframe, Evaluating RMSE and Rsq
Content: Machine Learning, Logistic regression steps, Probability matrix, Confusion matrix, Accuracy score, Recall value, Data preprocessing, Label encoding, Scaling the data, Splitting train test data, Running Logistic Regression, Y prediction on test data, Class imbalance, Type 1 & Type 2 errors.
Created my own web application that predicts Sales for broadcasting media
Content: Support Vector, Hyperplane, Hyperparameter, Non-linearly separable data, Scaling
Content: Unsupervised ML, Time Series analysis, Exponential Smoothing, Single Exponential smoothing, Holt Model, Holt Winter model, ARIMA model
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