LEARNING-AND-EXPERIMENTING_DATA-SCIENCE

| File | Summary | | --- | --- | | bookjson.txt | The code represents a directory tree for a data science learning and experimentation project. This includes resources addressing Machine Learning, Deep Learning, Feature Engineering, Data Preprocessing, Regression, Classification, Clustering, Neural Networks, and various visualization tools like Matplotlib and Seaborn. Moreover, it includes different project solutions and an example of a JSON file containing name, address, and phone data. | | JSON, XML, Dictionary, File.ipynb | The code represents a Jupyter notebook that covers data manipulation between common data formats: Dictionary, JSON, and XML. It demonstrates how to convert a dictionary into JSON format, write it to a text file, then read it back into a dictionary. The code also showcases how to perceive and write the dictionary into XML format using the dicttoxml Python library. | | 200 Machine Learning Projects Solved and Explained by Aman Kharwal Medium.mht | The code illustrates a directory tree of a wide-ranging Data Science learning and experimenting project. It includes machine learning projects, deep learning exercises like Keras, Tensorflow, activation functions and digit recognition models. Additionally, it contains feature engineering methods for outlier removal, standard deviation and Inter Quartile Range (IQR) calculations. It also provides preprocessing data, XGBoost implementation and regression techniques in the Machine Learning A-Z section. | | bookxml.txt | The directory tree represents a comprehensive Data Science learning and experimenting repository. It houses practice notebooks explaining Machine Learning, Deep Learning, Feature Engineering concepts, and python Data Science libraries: Numpy, Pandas, Matplotlib. It also includes exercises on data preprocessing, various types of regression, classification, clustering, and dimensionality reduction. ML A-Z section processes project-like assignments highlighting in-depth practical implementation. There's a separate section for projects, including a Real Estate Price Prediction exercise. The code shows xml data represented as a string. | | String,List,Dictionary,Tuple.ipynb | The Python code under'String,List,Dictionary,Tuple.ipynb' in the'Learning-and-Experimenting_Data-Science' directory demonstrates basic operations on Python data structures. It provides examples of string slicing, lists addition, removal, insertion, dictionary addition, deletion, iteration, and tuple creation with multiple data types. The immutability of string and tuples is also highlighted. |

| File | Summary | | --- | --- | | 10. Support Vector Machine (SVM) 1.ipynb 1.ipynb) | The provided code structure represents a collection of various Data Science and Machine Learning projects and exercises. Key areas covered include Deep Learning with exercises in Keras, TensorFlow, classification and activation functions. It also features Feature Engineering exercises focusing on outlier elimination and standard deviation. Additionally, it includes ML A-Z practices on data preprocessing, XGBoost, and regression. It also contains a notebook handling JSON, XML, and Dictionaries. | | 5. Dummy Variables & One Hot Encoding.ipynb | This code represents a directory tree of a Data Science learning and experimenting project. It includes machine learning projects, exercises and solutions centered around deep learning with TensorFlow & Keras, movie review classification, activation functions, and digit recognition. There are also exercises on outlier removal, z-score computation, and quartile usage in feature engineering. The project also has sections dedicated to data-preprocessing, XGBoost, various regression and classification algorithms in machine learning from scratch to advanced level. | | 2. Linear Regression With Multiple Variable.ipynb | This code represents a directory tree for a data science learning and experimentation project. It includes machine learning topics, featuring in-depth exercises and solutions on deep learning and feature engineering. It also covers pre-processing and outlier removal methods with examples. In-depth tutorials on various machine learning algorithms for regression and classification are provided alongside python code. The project also delves into Python libraries like Keras and TensorFlow and discusses concepts like activation functions and Z-scores. | | 6. Train,Test Split.ipynb | The code provides a directory structure for a Data Science learning project. It contains resources and exercises related to Machine Learning, Deep Learning, Feature Engineering, and data preprocessing, such as using Keras, TensorFlow, handling categorical data, outlier removal, standard deviation calculation, and regression models. The focus is broad, covering diverse tools and techniques, also extending to XGBoost and other areas like file handling. | | 13. K Means Clustering 1.ipynb | The provided directory tree represents a collection of projects and exercises in Data Science, Machine Learning, and Deep Learning. It includes resources like solutions for machine learning projects, exercises with Keras, TensorFlow, Google Colab, and handwritten digits recognition. Additionally, it contains guides about outlier removal, standard deviation, Z-score calculation, and interquartile range usage in Feature Engineering along with data preprocessing scripts in ML A-Z section. | | 8. Logistic Regression(Multi-class Classification) 2.ipynb 2.ipynb) | This code depicts a directory tree for a data science learning and experimentation project. It includes directories and files focused on deep learning solutions (Keras, TensorFlow), feature engineering techniques (outlier removal, standard deviation, Z-score, IQR), various machine learning projects such as regression and XGBoost, as well as some generic data preprocessing files. Additionally, there’s a file for handling JSON, XML, dictionary, and file inputs. | | 12. K Fold Cross Validation 2.ipynb | This code uses various machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, RandomForest) to classify the Iris dataset. It calculates and compares the models' average scores based on 7-fold Cross Validation. The best performing model, based on resulting scores, is the Support Vector Machine model. | | 13. K Means Clustering 2.ipynb | This code indicates a structured file directory for projects and resources centered on learning and experimenting with data science. Inside, there are materials on machine learning, deep learning, and feature engineering which include various practical exercises and solutions. Another section exists for data preprocessing, as part of a ML A-Z resource. There are also files focusing on more specific areas like outliers, standard deviation, Z-score, and Interquartile Range (IQR). | | 9. Decision Tree 1.ipynb | The code represents the directory structure of a data science learning and experimentation project. It includes solved machine learning projects, deep learning exercises involving Keras and Tensorflow, notes on feature engineering techniques for outlier removal and standardization, and files dealing with JSON, XML, and others. The ML A-Z folder contains files related to various machine learning topics like data preprocessing, XGBoost, and regression and classification techniques. | | 5. Dummy Variables & One Hot Encoding2.ipynb | The code depicts a directory structure for a data science learning and experimentation project. It includes work on machine learning with solved examples and exercises, deep learning exercises with Keras and TensorFlow, feature engineering techniques such as outlier removal and standard deviation, JSON/XML/Dictionary/File operations, and regression techniques. Notably, it contains a section dedicated to data preprocessing and an application of XGBoost. Some sections come with corresponding datasets. | | 11. Random Forest 2.ipynb | The code shows the directory structure for a data science learning and experimenting project. It contains solved machine learning projects, deep learning exercises involving Keras and Tensorflow, feature engineering notebooks detailing outlier removal methods, JSON and XML processing, and a comprehensive machine learning guide from data preprocessing to regression analysis. Additionally, there are specific exercises involving XGBoost and multiple linear regression. | | 15. GridSearchCV Hyper Parameter Tuning 2.ipynb | The code explores hyperparameter tuning in Python using GridSearchCV. It uses different machine learning models (SVM, Random Forest, Logistic Regression, GaussianNB, MultinomialNB, DecisionTree) with various hyperparameters to find the best model and its parameters. The best results are achieved with the SVM model using a linear kernel and C=1. | | 7. Logistic Regression(Binary Classification) 1.ipynb 1.ipynb) | The directory contains various data science and machine learning resources. It includes projects on deep learning using Keras and Tensorflow, featuring activities like movie review classification and handwritten digit recognition. It also contains exercises on Feature Engineering techniques such as outlier removal and normalization. The'ML A-Z' folder contains scripts for pre-processing data and various regression techniques. Lastly, there's a file explaining JSON, XML and Dictionary handling in Python. | | 9. Decision Tree 2.ipynb | The code represents a data science project directory with solutions and exercises. It includes machine learning projects and notebooks on different topics under deep learning and feature engineering such as Keras Sequential, movie review classification using TensorFlow, activation functions, handwritten digits recognition, and methods for outlier removal and understanding standard deviation & Z-score. | | 7. Logistic Regression(Binary Classification) 2.ipynb 2.ipynb) | This directory tree summarizes a set of data science learning resources and projects. It includes machine learning projects, deep learning exercises, feature engineering techniques such as outlier removal and use of Z-score, JSON/XML/Dictionary/File operations, and pre-processing and regression methodologies like XGBoost and polynomial regression in a ML A-Z folder. Each category contains Jupyter notebooks or Python scripts for practice and solutions. | | 15. GridSearchCV Hyper Parameter Tuning 1.ipynb | This code represents a directory tree for a Data Science repository that contains notebooks and scripts addressing various topics such as Machine Learning, Deep Learning, Feature Engineering and different types of regression and classification techniques. The repository includes solved exercises, project solutions and an array of datasets for hands-on practice. It also covers outlier removal, Z-score calculations, and advanced machine learning techniques including XGBoost. | | 3. GradientDescent and Costfunction1.ipynb | The code is a Jupyter notebook that contains a tutorial on implementing Gradient Descent and calculating Cost Function in Machine Learning. It explains theory and the algorithm of the Gradient Descent, aims to minimize the cost function, and demonstrates the partial derivatives-based formulas for determining the slope'm' and intercept'b' in linear regression. The code leverages markdown cells for the theoretical explanation and visual illustrations using images. | | 3. GradientDescent and Costfunction2.ipynb | This is a Data Science learning and experimenting directory tree, containing a variety of machine learning, deep learning, and feature engineering applications. It includes Abstracts of ML projects, Keras, TensorFlow exercises, outlier detection methods, encoding and decoding data files, various regression models, and XGBoost training. It also offers various related datasets and codes for data preprocessing and treats missing categories. | | 14. Naive Bayes 3.ipynb | The code provides a directory structure for a data science learning and experimenting project. It includes different sections: introduction and solution files for machine learning and deep learning projects, feature engineering techniques, and pre-processing templates. Specifically, it contains Jupyter notebooks focusing on deep learning exercises, outlier removal methods, standard deviation & IQR usage, and movie review classification. The final section, "ML A-Z", provides Python scripts for various data pre-processing techniques and machine learning algorithms such as regression, classification, and XGBoost. | | 9. Decision Tree 3.ipynb | The presented code illustrates a directory tree for a data science learning and experimenting project. This comprises a medium article on 200 solved machine learning projects, a deep learning folder with exercises and solutions using Keras, Tensorflow, and Google Colab, and a feature engineering directory addressing outlier removal and standard deviation calculation techniques. | | 8. Logistic Regression(Multi-class Classification) 1.ipynb 1.ipynb) | The code represents a directory structure for a Data Science learning and experimentation project. It includes folders for machine learning projects, deep learning exercises with Keras and TensorFlow, feature engineering techniques like outlier removal and standard deviations, and data preprocessing. It also involves work with various data types like JSON, XML, dictionaries, and files. | | 10. Support Vector Machine (SVM) 2.ipynb 2.ipynb) | The directory tree illustrates a repository dedicated to Machine Learning and Data Science learning and experimentation. It includes solved projects, dedicated folders for deep learning concepts, with Keras and Tensorflow exercises, and feature engineering techniques. It also includes specific preprocessing, regression, and XGBoost application examples, in Machine Learning from A-Z folder. Files are a mix of Python scripts and Jupyter notebooks, some of which include solutions to exercises. | | 12. K Fold Cross Validation 1.ipynb | The provided code is a machine learning notebook that explains the implementation of K-Fold Cross Validation in Python. Key features include visually explaining the concept, implementing K-Fold Cross Validation for Logistic Regression, SVM, and Random Forest models using a digits dataset. It also demonstrates parameter tuning to optimize the number of estimators in the RandomForestClassifier. Further, it showcases the use of the crossvalscore function to automate the cross-validation process. | | 4. Saving Model Using Pickle and sklearn joblib.ipynb | The given code focuses on creating a linear regression model based on a housing prices dataset and then demonstrates two methods to save and load the trained model: Python's Pickle and Scikit-learn's joblib. The model is successfully loaded back from the saved state and used to make predictions, confirming that the saving and loading methods are effective. | | 1. Linear Regression With One Variable.ipynb | This code outlines the directory structure for a data science project with resources for learning and experimenting. It includes files for machine learning projects, deep learning exercises involving Keras, TensorFlow for movie review classification, activation functions, etc. In Feature Engineering, outliers are removed using percentile, SD/Z-Score and IQR. Additionally, it contains files related to data processing and handling in various formats. | | 3. GradientDescent and Costfunction3.ipynb | This code represents a directory structure for a data science project, showcasing machine learning examples, deep learning exercises with Keras and Tensorflow, feature engineering examples, and data processing scripts. It includes practice notebooks for outlier handling, SD, and IQR use, files for data preprocessing, regression models in the "ML A-Z" subdirectory and XGBoost datasets and scripts. Additionally, it also handles JSON, XML, and file processing, each encapsulating different aspects of data science learning and experimenting. | | 14. Naive Bayes 2.ipynb | The given code is part of a Python-based Naive Bayes machine learning model to predict email spam. It imports a data set of emails, preprocesses the text data into numerical values using a Count Vectorizer, and encodes "spam" as 1 in a new "spam" column. It splits the data into training and test subsets and trains the model. Furthermore, it uses a pipeline to streamline the fit and score process. Finally, it applies the model on testing data to evaluate its performance and to predict spam on new data. | | 14. Naive Bayes 1.ipynb | This is a Data Science learning and experimentation directory tree. It contains various Machine Learning and Deep Learning projects, Feature Engineering exercises, JSON/XML handling methods, and ML tasks broken down into sections such as data preprocessing, regression, classification, and XGBoost. Each section comprises exercise files, solutions, and relevant datasets. It seems to be an organized repository of myriad data science concepts, techniques, and solution approaches. | | 11. Random Forest 1.ipynb | The code describes the structure of a data science learning and experimenting repository. This includes machine learning projects with explanations, notebooks in the Deep Learning and Feature Engineering subdirectories with related exercises and solutions, and a special focus on outlier removal and normalization techniques. Categorization and data preprocessing sample in Python files are also present, supplementary notebooks about JSON, XML, Dictionary, Files management are included too. |

| File | Summary | | --- | --- | | Home Price Prediction.ipynb | The code presents a directory structure for a Data Science learning and experimenting project. There are four main sections: a Medium article on machine learning projects, deep learning exercises using Keras, Tensorflow and Google Colab, feature engineering notebooks on outlier removal, standard deviation and IQR, and a machine learning module from A-Z that covers data preprocessing and regression. There is also specific handling of JSON, XML, dictionary, and file-related operations. | | columns.json | The provided code is a JSON object containing data columns for a Real Estate Price Prediction project. It defines the features of properties, predominantly areas in and around Bangalore, India. It includes specifics like total square footage, number of baths and bedrooms, and various neighborhood locations. These data columns can potentially be used as inputs for a predictive model to forecast real estate prices. |

| File | Summary | | --- | --- | | 3. Bar chart.ipynb | The code represents a hierarchical directory tree for a Data Science learning project. It contains resources explaining machine learning and deep learning, including notebooks on keras, tensorflow, and activation function. Another section focuses on feature engineering topics like outliers removal, standard deviation, and the use of IQR. Included are explanation files, exercise notebooks, and corresponding solutions. There's also a section focusing on file types in data science (JSON, XML, etc). | | 4. Histograms.ipynb | The code represents a directory structure for a data science learning and experimentation project. It contains resources and exercises for machine learning, deep learning, and feature engineering via Jupyter notebooks (.ipynb). The deep learning section covers topics like Keras Sequentials, Tensorflow, activation functions, and handwritten digits recognition. Feature engineering includes outlier removal, Z-score and Standard deviation applications, and Interquartile range (IQR) usage. It also contains unspecified resources related to JSON, XML, Dictionaries, and Files. | | 1. Format String in plot.ipynb | The provided code exemplifies a directory tree for a data science learning and experimentation repository. It features resources like a medium article and notebooks for experiments on Machine Learning, Deep Learning and Feature Engineering topics. These topics include implementing Keras Sequential Models, classification, understanding activation functions, outlier removal, working with standard deviation, and Z-score exercises. | | 6. Save Chart.ipynb | The code represents a directory setup for a data science project, including solved machine learning projects, deep learning exercises involving Keras, Tensorflow, and activation functions, as well as feature engineering techniques like outlier removal, standard deviation and Z-score calculations. It also contains a dataset for use in these tasks. | | 5. Pie Chart.ipynb | The directory contains materials for learning Data Science, specifically Machine Learning and Deep Learning. There are solved projects and explanations, solutions to Keras sequential exercise, and lessons on movie review classification using Tensorflow and Google Colab. Also featured are notebooks on classifying handwritten digits, feature engineering techniques like outliers removal using percentile, standard deviation, Z-score applications, and usage of Interquartile Range (IQR). | | 7. Subplot.ipynb | The code displays a basic tutorial for creating subplots using Matplotlib, specifically creating a bar chart of income over four years. It introduces the concept of a subplot: a secondary section of the main plot. The dataset includes details for years 2014-2017 and their respective earnings and expenses. However, only the income bar chart is displayed in the output. | | 2. Axes Labels, Legend, Grid.ipynb | The code outlines a directory tree for a Data Science project. It includes a document with the explanation of 200 solved Machine Learning projects and notebooks with different Deep Learning and Feature Engineering exercises and their solutions. Deep Learning exercises cover Keras, TensorFlow, activation functions, and digit recognition, while Feature Engineering folders include techniques to remove outliers and standard deviation, Z-score functionalities. |

| File | Summary | | --- | --- | | 2.0 Standard Deviation, Z-score.ipynb | The code represents a directory structure for a data science learning and experimenting project. It includes machine learning project solutions, deep learning notebooks for different exercises including Keras, Tensorflow and Handwritten digits recognition. Also exercises on feature engineering using techniques like removing outliers, standard deviation, Z-score, IQR, and data preprocessing. There's also JSON, XML, Dictionary, File notebooks. The ML A-Z folder contains scripts for data preprocessing. | | 1.0 Removing Outlier using Percentile.ipynb | The provided directory tree represents a collection of data science learning and experimenting projects, segregated into categories of machine learning, deep learning, feature engineering, and handling different file formats. It contains Python programs and Jupyter notebooks exhibiting implementations of diverse machine learning algorithms, data preprocessing techniques, outlier handling methods, and neural network modeling. It includes projects dealing with regression, classification, XGBoost models, and various data handling techniques. | | 1.1 Removing Outlier using Percentile Exercise Solution.ipynb | The provided directory contains resources for learning and experimenting with Data Science. It includes projects and exercises on Machine Learning, Deep Learning, Feature Engineering, and data preprocessing techniques. It also contains various Python scripts for regression, classification, clustering, and XGBoost models. The exercises employ libraries like Keras and Tensorflow for deep learning and provide solutions for removing outliers, using IQR, standard deviation and Z-score. | | 2.1 Standard Deviation, Z-score Exercise Solution.ipynb | The directory tree details a collection of Data Science and Machine Learning projects. The code includes various project files dealing with deep learning methods with Keras and Tensorflow, feature engineering techniques, and Data Preprocessing. It also contains a tutorial and solution of Machine Learning Python projects on XGBoost, and data handling in different formats like JSON, XML, and Dictionary. | | 3.0 Using IQR.ipynb | This Python code uses pandas to remove outliers in a dataset pertaining to heights through an Interquartile Range (IQR) method. The script calculates the first (Q1) and third quartiles (Q3) to establish an IQR. Outliers are then determined as values that fall below Q1-1.5IQR or above Q3+1.5IQR. The outliers are dropped to create a cleaned dataset. |

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