### Run Training Pipeline with Configuration

Source: https://github.com/niketkumardheeryan/ml-capsule/wiki/_Sidebar

Example command to start a model training pipeline using a specified configuration file. Ensure the path to the script and config file are correct.

```bash
python src/train_model.py --config config.yaml
```

--------------------------------

### Setup and Run Chicken Disease Classification Pipeline

Source: https://context7.com/niketkumardheeryan/ml-capsule/llms.txt

Commands to set up the Python environment, install dependencies, initialize DVC, and run the training pipeline. Includes instructions for starting the MLflow experiment tracker.

```bash
# 1. Setup environment
conda create -n cnncls python=3.8 -y && conda activate cnncls
pip install -r requirements.txt

# 2. Run full training pipeline (DVC)
dvc init && dvc repro
# OR run stages individually:
python main.py

# 3. Start MLflow experiment tracker
export MLFLOW_TRACKING_URI=https://dagshub.com/<user>/project.mlflow
export MLFLOW_TRACKING_USERNAME=<user>
export MLFLOW_TRACKING_PASSWORD=<token>
mlflow ui   # browse at http://localhost:5000
```

--------------------------------

### Python Environment Setup

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Machine Hack -1.ipynb

This code block sets up a Python 3 environment on Kaggle, including comments on how to list input files and save output. It's a standard starting point for Kaggle notebooks.

```python
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

#import numpy as np # linear algebra
#import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

#import os
#for dirname, _, filenames in os.walk('/kaggle/input'):
  #  for filename in filenames:
    #    print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

```

--------------------------------

### Clone and Install iconv-lite

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Social Media Fake Accounts Detection with Interactive UI/server/node_modules/iconv-lite/README.md

Clone the repository, navigate to the directory, and install dependencies using npm.

```bash
git clone git@github.com:ashtuchkin/iconv-lite.git
cd iconv-lite
npm install
```

--------------------------------

### Basic GET Request with Needle

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Social Media Fake Accounts Detection with Interactive UI/server/node_modules/needle/README.md

Demonstrates how to perform a basic GET request using the Needle library. Ensure Needle is installed and imported before use.

```javascript
var needle = require('needle');

needle.get('http://www.google.com', function(error, response, body) {
  console.log('Got response: ', response.statusCode);
});
```

--------------------------------

### Install Necessary Libraries

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Bidirectional LSTM/README.md

Installs the required Python libraries for the project. Ensure these are installed before running the code.

```bash
pip install pandas
pip install tensorflow
pip install numpy
pip install sklearn
pip install keras
```

--------------------------------

### Install Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Indian Elections/Election_prediction-WebApp/Readme.md

Installs the necessary Python libraries for the application. Ensure you have Python and pip installed.

```bash
pip install streamlit pandas numpy seaborn scikit-learn matplotlib
```

--------------------------------

### Install LightGBM

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Machine Hack -1.ipynb

Installs the LightGBM library. This is a prerequisite for using LightGBM models.

```python
#!pip install lightgbm
```

--------------------------------

### Clone Repository and Install Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Lane Line Detection [OPEN CV]/README.md

Instructions for cloning the project repository and installing necessary Python packages.

```bash
git clone https://github.com/your_username/Lane-Line-Detection.git
cd Lane-Line-Detection
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Skin Disease Predictor/README.md

Installs the necessary Python libraries for the project. Ensure these are installed before running the project.

```bash
pip install keras
pip install tensorflow
pip install numpy
pip install glob
```

--------------------------------

### Install pyttsx3 Library

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Sign-Language-Translator/code file sign language translator.ipynb

Installs the pyttsx3 library, used for text-to-speech conversion to vocalize translated sign language.

```bash
pip install pyttsx3
```

--------------------------------

### Install CatBoost

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Machine Hack -1.ipynb

Installs the CatBoost library. This is required for using CatBoost models.

```python
#!pip install catboost
```

--------------------------------

### Install Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Employee_attrittion_prediction_using_ML/readme.md

Install all necessary Python packages listed in the requirements.txt file.

```sh
pip install -r requirements.txt
```

--------------------------------

### Install PyCaret

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Statistical_modeling_python/Statistical_modeling.ipynb

Installs the PyCaret library using pip. Ensure you have pip installed and updated.

```bash
pip install pycaret
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Poker Hand Prediction/README.md

Installs necessary Python libraries for the project using pip. Ensure these are installed before running the notebook.

```bash
pip install pandas numpy scikit-learn matplotlib seaborn
```

--------------------------------

### Install PyTorch

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Machine Hack -1.ipynb

Installs the PyTorch library. This command is executed in a shell environment.

```bash
! pip install torch
```

--------------------------------

### Install Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Artifical neural network from scratch/README.md

Install the required dependencies for the project using pip.

```bash
pip install -r requirements.txt
```

--------------------------------

### Install Dependencies with Pip

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/MNIST English Classification/Readme.md

Installs the necessary Python libraries for the project. Ensure you have pip installed.

```bash
pip install torch torchvision numpy pandas seaborn matplotlib scikit-learn

```

--------------------------------

### Install dotenv with npm

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Social Media Fake Accounts Detection with Interactive UI/server/node_modules/dotenv/README.md

Install dotenv locally to your project using npm. This is the recommended installation method.

```bash
npm install dotenv --save
```

--------------------------------

### Install and Import Libraries

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Web-Scraping-with-Beautiful-Soup-master/scrape_amazon_reviews.ipynb

Installs and imports necessary libraries for web scraping, including requests and BeautifulSoup. Ensure these are installed before running the script.

```python
import requests
from bs4 import BeautifulSoup
```

--------------------------------

### Install DBGenie Package

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/PyPI Package/README.md

Install the DBGenie package using pip. This command fetches and installs the latest version from the Python Package Index.

```bash
pip install dbgenie2
```

--------------------------------

### Install Dependencies and Run Tests

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Social Media Fake Accounts Detection with Interactive UI/server/node_modules/cors/CONTRIBUTING.md

Before submitting a pull request, ensure you have installed the necessary dependencies and that all tests pass.

```bash
npm install
npm test
```

--------------------------------

### Install mediapipe Library

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Sign-Language-Translator/code file sign language translator.ipynb

Installs the mediapipe library, which is essential for hand tracking and gesture recognition.

```bash
pip install mediapipe
```

--------------------------------

### Install apyori library

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/market-basket-recommendation/Apriori.ipynb

Installs the apyori library, which is necessary for implementing the Apriori algorithm.

```python
!pip install apyori
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Automated Financial Reporting with Deep Learning/readme.md

Installs the necessary Python packages for the project. Ensure you have pip available.

```bash
pip install pandas numpy scikit-learn tensorflow
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Spam Mail Classifier/README.md

Clone the project repository and navigate into the project directory. This is the first step for local setup.

```sh
git clone https://github.com/yourusername/spam-mail-detection.git
cd spam-mail-detection
```

--------------------------------

### Install PyCaret Library

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Machine Hack -1.ipynb

Installs the PyCaret library, a low-code machine learning tool, using pip.

```python
#! pip install pycaret
```

--------------------------------

### Install tld library

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Malicious Url Detection/MaliciousUrlDetection.ipynb

Installs the 'tld' library, which is used for extracting the Top Level Domain from URLs.

```bash
!pip install tld
```

--------------------------------

### Run the Application

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Chicken_Disease_Classification/README.md

Execute the main application script to start the project. Access the application via your local host and port.

```bash
python app.py
```

```bash
open up you local host and port
```

--------------------------------

### Get Unique Start Locations

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/UBER_DATA_ANALYSIS.ipynb

Extracts the 'START*' column, removes any missing values, and converts it into a set to get a collection of unique starting locations. Useful for understanding the geographical scope of the rides.

```python
start= df['START*'].dropna()
un_start=set(start)
un_start
```

--------------------------------

### Install TensorFlow

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Sudoku Solver using CNN/sudoku.ipynb

Installs the TensorFlow library, which is required for building and running the CNN model.

```bash
pip install tensorflow
```

--------------------------------

### Install scikit-optimize

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/HyperParameter Tuning/Bayesian Optimization/GaussianProcesses.ipynb

Installs the scikit-optimize library, which is used for hyperparameter tuning.

```bash
!pip install scikit-optimize
```

--------------------------------

### Promise-based GET Request Example

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Social Media Fake Accounts Detection with Interactive UI/server/node_modules/needle/README.md

An example of making a GET request using Promises. The response and potential errors are handled using .then() and .catch().

```javascript
// using promises
needle('get', 'https://server.com/posts/123')
  .then(function(resp) {
    // ...
  })
  .catch(function(err) {
    // ...
  });

```

--------------------------------

### Clone the Repository

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/SQL injection detection/readme.md

Use this command to download the project files. Navigate into the cloned directory to proceed with setup.

```bash
git clone https://github.com/yourusername/sql-injection-detection.git
cd sql-injection-detection
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Dark Pattern Detection/Readme.md

Instructions for cloning the project repository and navigating into the project directory.

```bash
git clone 
cd Dark Pattern Detection
```

--------------------------------

### Basic GET Request with Callback

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Social Media Fake Accounts Detection with Interactive UI/server/node_modules/needle/README.md

Performs a simple HTTP GET request and logs the response body if successful. This is a fundamental example for fetching data from a URL.

```javascript
var needle = require('needle');

needle.get('http://www.google.com', function(error, response) {
  if (!error && response.statusCode == 200)
    console.log(response.body);
});
```

--------------------------------

### Install Requests Package

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Web-Scraping-with-Beautiful-Soup-master/README.md

Use pip to install the requests library for making HTTP requests.

```sh
pip install requests
```

--------------------------------

### Get Scikit-learn Version

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Plagiarism-detector-using-machine-learning-main/Building Plagiarism checker using Machine Learning.ipynb

Retrieves and prints the currently installed version of the scikit-learn library.

```python
# sklearn version
import sklearn
sklearn.__version__
```

--------------------------------

### Install Kaggle API and Authenticate

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Defective Captcha Image Recognition/Models/models_Defective_Captcha_Image_Recognition_.ipynb

Installs the Kaggle API, uploads Kaggle credentials, and sets up the necessary directory permissions for downloading datasets.

```python
!pip install -q kaggle
from google.colab import files
files.upload()
!mkdir ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
```

--------------------------------

### Get User Input to Start or End Game

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Rock Paper Scissors Python Game/RockPaperScissorsPythonGame.ipynb

Prompts the user to start the game by entering '1' or end the game by entering '2'. Includes input validation to ensure only '1' or '2' is accepted.

```python
x = int(input("(1)Start. (2)End the game. \n"))

if x != 1 and x != 2:
    x = int(input("Invalid input! Please enter '1' or '2': \n"))
```

--------------------------------

### Install NLTK using pip

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/NLTK Documentation/README.md

Use this command to install the NLTK library on your local system.

```bash
pip install nltk
```

--------------------------------

### Stemming Example

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/NLP-Project/README.md

Tokenizes a sentence and then applies stemming to each word to get its root form. Requires 'punkt' to be downloaded.

```python
string_for_stemming = "The crew of the USS Discovery discovered many discoveries. Discovering is what explorers do."
words = word_tokenize(string_for_stemming)
words
```

```python
stemmed_words = [stemmer.stem(word) for word in words]
stemmed_words
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Diseases_Prediction/Readme.md

Clone the project repository and change the directory to the project's root. This is the initial step before installing dependencies.

```bash
git clone https://github.com/Niketkumardheeryan/ML-CaPsule.git
cd ML-CaPsule
```

--------------------------------

### Install Contractions Library

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Sentiment-Analysis/Sentiment_Analysis_Major_Project.ipynb

Installs the 'contractions' library, which is used for expanding contracted words in text.

```bash
!pip install contractions
```

--------------------------------

### Configuration Manager for Training and Callbacks

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Chicken_Disease_Classification/research/04_training.ipynb

Manages the loading of configuration and parameters, and creates necessary directories. Use this to initialize and retrieve configurations for preparing callbacks and training.

```python
import os
from dataclasses import dataclass
from pathlib import Path
import tensorflow as tf

# Assuming TrainingConfig and PrepareCallbacksConfig are defined as above

class ConfigurationManager:
    def __init__(
        self,
        config_filepath = CONFIG_FILE_PATH,
        params_filepath = PARAMS_FILE_PATH):
        self.config = read_yaml(config_filepath)
        self.params = read_yaml(params_filepath)
        create_directories([self.config.artifacts_root])



    def get_prepare_callback_config(self) -> PrepareCallbacksConfig:
        config = self.config.prepare_callbacks
        model_ckpt_dir = os.path.dirname(config.checkpoint_model_filepath)
        create_directories([
            Path(model_ckpt_dir),
            Path(config.tensorboard_root_log_dir)
        ])

        prepare_callback_config = PrepareCallbacksConfig(
            root_dir=Path(config.root_dir),
            tensorboard_root_log_dir=Path(config.tensorboard_root_log_dir),
            checkpoint_model_filepath=Path(config.checkpoint_model_filepath)
        )

        return prepare_callback_config




    def get_training_config(self) -> TrainingConfig:
        training = self.config.training
        prepare_base_model = self.config.prepare_base_model
        params = self.params
        training_data = os.path.join(self.config.data_ingestion.unzip_dir, "Chicken-fecal-images")
        create_directories([
            Path(training.root_dir)
        ])

        training_config = TrainingConfig(
            root_dir=Path(training.root_dir),
            trained_model_path=Path(training.trained_model_path),
            updated_base_model_path=Path(prepare_base_model.updated_base_model_path),
            training_data=Path(training_data),
            params_epochs=params.EPOCHS,
            params_batch_size=params.BATCH_SIZE,
            params_is_augmentation=params.AUGMENTATION,
            params_image_size=params.IMAGE_SIZE
        )

        return training_config
```

--------------------------------

### Get TensorFlow Version

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Pneumonia Classification using Chest X-Ray/Model/pneumonia_classification.ipynb

Retrieves and prints the installed TensorFlow version. This is useful for ensuring compatibility with libraries and model architectures.

```python
tf.__version__
```

--------------------------------

### Install Keras

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Sudoku Solver using CNN/sudoku.ipynb

Installs the Keras library, a high-level API for neural networks, often used with TensorFlow.

```bash
pip install keras
```

--------------------------------

### Custom Accept Header and Deflate Compression

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Social Media Fake Accounts Detection with Interactive UI/server/node_modules/needle/README.md

Example of making a GET request with a custom Accept header and enabling deflate compression.

```APIDOC
## Custom Accept header, deflate

### Description
Makes a GET request with a custom Accept header and enables deflate compression.

### Method
GET

### Endpoint
`api.github.com/users/tomas`

### Parameters
#### Request Body
- **compressed** (boolean) - Optional - Set to `true` to enable deflate compression.
- **follow** (number) - Optional - The maximum number of redirects to follow.
- **accept** (string) - Optional - The value for the Accept header.

### Request Example
```js
var options = {
  compressed : true,
  follow     : 10,
  accept     : 'application/vnd.github.full+json'
}

needle.get('api.github.com/users/tomas', options, function(err, resp, body) {
  // body will contain a JSON.parse(d) object
  // if parsing fails, you'll simply get the original body
});
```
```

--------------------------------

### JavaScript for DOM Ready Installation

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Dark Pattern Detection/Tesseract-OCR/unicharambigs.5.html

This JavaScript function installs footnote and table of contents generation logic. It uses `setInterval` to periodically check for DOM readiness and then calls the respective functions. It also attaches an event listener for `DOMContentLoaded` or `window.onload` for initial setup.

```javascript
var timerId;
function reinstall() {
  asciidoc.footnotes();
  if (toclevels)
    asciidoc.toc(toclevels);
}
function reinstallAndRemoveTimer() {
  clearInterval(timerId);
  reinstall();
}
timerId = setInterval(reinstall, 500);
if (document.addEventListener)
  document.addEventListener("DOMContentLoaded", reinstallAndRemoveTimer, false);
else
  window.onload = reinstallAndRemoveTimer;

```

--------------------------------

### Instantiate and Get Callback List

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Chicken_Disease_Classification/research/03_prepare_callbacks.ipynb

Instantiates the ConfigurationManager and PrepareCallback classes, then retrieves the list of callbacks. Includes basic error handling.

```python
try:
    config = ConfigurationManager()
    prepare_callbacks_config = config.get_prepare_callback_config()
    prepare_callbacks = PrepareCallback(config=prepare_callbacks_config)
    callback_list = prepare_callbacks.get_tb_ckpt_callbacks()
    
except Exception as e:
    raise e
```

--------------------------------

### Load Seaborn Dataset and Display Head

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Visualization  with Seaborn & Matplotlib.ipynb

Imports the Seaborn library and loads the 'tips' dataset. Use this to get started with sample data for visualization.

```python
import seaborn as sns                 # importing seaborn library 

#getting data inbuilt

df=sns.load_dataset('tips')          # Dataset
df.head()
```

--------------------------------

### Get Dataset Index Locations

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Covid_Third_Wave_Forecasting/covid-19-forecasting-using-arima.ipynb

This snippet retrieves the start and end indices for specific dates within a dataset. It's useful for slicing time series data.

```python
start = dataset.index.get_loc('2020-05-1')
end = dataset.index.get_loc('2022-02-02')
print(start,'to',end)
```

--------------------------------

### DVC Commands

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Chicken_Disease_Classification/README.md

Initialize DVC, reproduce experiments, and view the DVC DAG.

```bash
dvc init
```

```bash
dvc repro
```

```bash
dvc dag
```

--------------------------------

### Clone Repository and Install Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/face-recognition/Readme.md

Clone the ML-CaPsule repository and install the required Python packages using pip.

```bash
git clone https://github.com/Raghucharan16/ML-CaPsule.git
cd ML-CaPsule/face-recognition
```

```bash
pip install -r requirements.txt
```

--------------------------------

### Initialize and Reset Hedging Environment

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Dynamic Hedging Strategies using Reinforcement Learning/Dynamic_Hedging_StrategiesReinforcementLear.ipynb

Initializes the hedging environment with provided data and resets it to get the initial observation. This is a common starting point for any reinforcement learning task.

```python
env = HedgingEnv(data)
initial_observation = env.reset()
print(initial_observation)
```

--------------------------------

### Import Libraries and Setup

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Chronic Kidney Disease Prediction/Chronic_kidney_disease_prediction.ipynb

Imports necessary libraries for data manipulation, visualization, and machine learning. Sets up plotting style and display options.

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

import warnings
warnings.filterwarnings('ignore')

plt.style.use('fivethirtyeight')
%matplotlib inline
pd.set_option('display.max_columns', 26)

from mlxtend.plotting import plot_confusion_matrix
```

--------------------------------

### Starting TensorBoard for Visualization

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Football_Analyser_using_YOLO/training/Football_training_yolov5.ipynb

This command initiates TensorBoard, a visualization tool for machine learning experiments. It allows you to monitor training metrics, view model graphs, and analyze results. The log directory specifies where training logs are stored.

```bash
tensorboard --logdir runs/detect/train2 --view at http://localhost:6006/
```

--------------------------------

### Initialize RL Agent and Environment

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Dynamic Hedging Strategies using Reinforcement Learning/Dynamic_Hedging_StrategiesReinforcementLear.ipynb

Sets up the state and action sizes for the RL agent and initializes the DQNAgent. This is a prerequisite for training.

```python
state_size = 5
action_size = 3
agent = DQNAgent(state_size, action_size)
```

--------------------------------

### Update Many Documents in PyMongo

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Basics of ML and DL/ML/pymongo tutorial.ipynb

Modify all documents that match the specified query criteria using `update_many()`. This example uses a regular expression to find names starting with 'S' and updates their name.

```python
myquery = { "name": { "$regex": "^S" } }
newvalues = { "$set": { "name": "Minnie" } }

collection.update_many(myquery, newvalues)
```

--------------------------------

### Run Objective Function with Default Hyperparameters

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/HyperParameter Tuning/Bayesian Optimization/SMAC(SequentialModelBasedAlgorithmConfiguration).ipynb

Executes the defined objective function with a set of default hyperparameters to test the setup. This is useful for verifying the function's behavior before starting the full optimization.

```python
# Ppassing some default hyper-parameters.

default_parameters = [1e-5, 1, 1, 16, 'relu']

objective(x=default_parameters)
```

--------------------------------

### Initialize LightGBM Parameters and Variables

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/ensemble-methods-notebooks-master/Ch5.5-case-study-document-retrieval.ipynb

Imports necessary libraries and defines fixed parameters for LightGBM, including early stopping and evaluation metrics. It also sets up variables for hyperparameter tuning and performance tracking.

```python
# LightGBM has numerous parameters that we want to optimize over
import numpy as np
from scipy.stats import randint, uniform
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import accuracy_score
import lightgbm as lgb
import time

fixed_params = {'early_stopping_rounds': 25, 
                'eval_metric' : 'multi_logloss', 
                'eval_set' : [(Xtst, ytst)],
                'eval_names': ['test set'],
                'verbose': 100}

num_random_iters = 20
num_cv_folds = 5

cv_scores = {}
tst_scores = {}
run_time = {}
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/MNIST English Classification/Readme.md

Clones the ML-CaPsule repository and navigates into the specific project directory. This is the first step to set up the project locally.

```bash
git https://github.com/Niketkumardheeryan/ML-CaPsule.git
cd MNIST English Classification
cd CNN
cd .py files

```

--------------------------------

### Get File Dataset Loader

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Music Generation using LSTM/Music_Generation.ipynb

Initializes a dataset loader for audio files using glob patterns. Configures parameters for audio processing, batching, and shuffling. This function is a starting point for creating a data pipeline.

```python

def get_files_dataset(
        glob_location,
        total_seconds=2,
        out_len = 3.3,
        hop_size=1,
        max_feats = 2048,
        batch_size=4,
        shuffer_size=1000,
        scale=1,
        rate=10_000,
        mdct_feats=256
    ):
    """Get file dataset loader for a glob of audio files."""
    
    files = glob(
        glob_location,
        recursive=True
    )
    
```

--------------------------------

### Install PyTorch Tabular (Basic)

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/pytorch_tabular/pytorch_tabular_tutorial.ipynb

Installs the basic PyTorch Tabular library. Use this if you don't need all optional dependencies.

```bash
pip install pytorch_tabular
```

--------------------------------

### Python range() Function Usage

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Basics of the Python/Basic of Python/2.TUPLES_LECTURE/LISTS_AND_RANGE.ipynb

Shows how to use the range() function to generate a list of numbers. The range() function takes start, stop, and step arguments. This example generates odd numbers from 1 up to (but not including) 10.

```python
list(range(1,10,2))
```

--------------------------------

### Tkinter GUI Setup and Send Function

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/CBT_ChatBot/CBT_ChatBot.ipynb

Initializes the Tkinter GUI for the chatbot, including the chat log, entry box, and send button. The send function handles user input, displays messages, and gets bot responses.

```python
#Creating GUI with tkinter
import tkinter
from tkinter import *


def send():
    msg = EntryBox.get("1.0",'end-1c').strip()
    EntryBox.delete("0.0",END)

    if msg != '':
        ChatLog.config(state=NORMAL)
        ChatLog.insert(END, "You: " + msg + '\n\n')
        ChatLog.config(foreground="#442265", font=("Verdana", 12 ))

        res = chatbot_response(msg)
        ChatLog.insert(END, "Bot: " + res + '\n\n')

        ChatLog.config(state=DISABLED)
        ChatLog.yview(END)


base = Tk()
base.title("Aishika")
base.geometry("400x500")
base.resizable(width=FALSE, height=FALSE)

#Create Chat window
ChatLog = Text(base, bd=0, bg="white", height="8", width="50", font="Arial",)

ChatLog.config(state=DISABLED)

#Bind scrollbar to Chat window
scrollbar = Scrollbar(base, command=ChatLog.yview, cursor="heart")
ChatLog['yscrollcommand'] = scrollbar.set

#Create Button to send message
SendButton = Button(base, font=("Verdana",12,'bold'), text="Send", width="12", height=5,
                    bd=0, bg="#32de97", activebackground="#3c9d9b",fg='#ffffff',
                    command= send )

#Create the box to enter message
EntryBox = Text(base, bd=0, bg="white",width="29", height="5", font="Arial")
#EntryBox.bind("<Return>", send)


#Place all components on the screen
scrollbar.place(x=376,y=6, height=386)
ChatLog.place(x=6,y=6, height=386, width=370)
EntryBox.place(x=128, y=401, height=90, width=265)
SendButton.place(x=6, y=401, height=90)

base.mainloop()
```

--------------------------------

### Install PyTorch Geometric and Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Molecule_Property_Analysis/molecule_property_analysis.ipynb

Installs PyTorch Geometric and its required dependencies. Ensure PyTorch is installed first, as the installation command is version-specific.

```python
import torch
pyg_url = f"https://data.pyg.org/whl/torch-{torch.__version__}.html"
%pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f $pyg_url
```

--------------------------------

### Install required packages

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/market-basket-recommendation/readme.md

Install all necessary Python packages for the project by running this command. Ensure you have a 'req.txt' file in your project directory.

```bash
pip install -r req.txt
```

--------------------------------

### Initialize Teachable Machine Model and Webcam

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Google Teachable Machine/Deployment/deploy_model.html

Loads the Teachable Machine image model and sets up the webcam. Ensure the model and metadata URLs are correctly specified. The webcam is configured to be 200x200 pixels and flipped horizontally.

```javascript
const URL = "../Model/keras_model.h5";
let model, webcam, labelContainer, maxPredictions;

async function init() {
    const modelURL = URL + "model.json";
    const metadataURL = URL + "metadata.json";

    model = await tmImage.load(modelURL, metadataURL);
    maxPredictions = model.getTotalClasses();

    const flip = true;
    webcam = new tmImage.Webcam(200, 200, flip);
    await webcam.setup();
    await webcam.play();
    window.requestAnimationFrame(loop);

    document.getElementById("webcam-container").appendChild(webcam.canvas);
    labelContainer = document.getElementById("label-container");
    for (let i = 0; i < maxPredictions; i++) {
        labelContainer.appendChild(document.createElement("div"));
    }
}
```

--------------------------------

### Create a virtual environment

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/market-basket-recommendation/readme.md

It is recommended to create a virtual environment to manage project dependencies. Activate it using the provided command based on your operating system.

```bash
python3 -m venv venv
source venv/bin/activate  # On Windows, use venv\Scripts\activate
```

--------------------------------

### Python String Basics: Creation and Properties

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Basics of ML and DL/ML/Strings.ipynb

Demonstrates how to create strings, print them, check their type, and get their length. Also shows how to access individual characters using indexing.

```python
# strings and its properties

a = "this is a string"

print(a)

print(type(a))

print(len(a))

print(a[0],a[len(a)-1])
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Medical Insurance Cost Prediction/README.md

Installs necessary Python libraries for the project using pip. Ensure you have Python 3.x installed.

```bash
pip install numpy pandas matplotlib seaborn scikit-learn jupyter
```

--------------------------------

### PyScript Installation

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/PyScript Tutorial/README.md

Reference PyScript using HTML script tags for installation. This is typically done in the head section of your HTML file.

```html
<link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" />
<script defer src="https://pyscript.net/alpha/pyscript.js"></script>
```

--------------------------------

### Install PyCaret

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/AutoML_GSSoC.ipynb

Installs the PyCaret library, a low-code machine learning framework. Ensure you have a stable internet connection for the download.

```python
!pip install pycaret

```

--------------------------------

### Install Project Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/GUI-JARVIS/README.md

Installs the required packages and libraries for the AI-JARVIS project. Ensure Python 3.9.2 or higher is installed.

```bash
pip install pyttsx3
pip install random
pip install re
pip install smtplib
pip install winsound
pip install wikipedia
pip install sys
pip install os
pip install webbrowser
pip install datetime
pip install speech_recognition
pip install urllib
pip install PyQt5
pip install geopy
pip install bs4
pip install yahoo_fin
pip install stock_info
```

--------------------------------

### Install Python Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Foreign Exchange Rate Prediction/Readme.md

Installs the necessary Python libraries for the project using pip. Ensure these are installed before running the project.

```bash
pip install keras
pip install tensorflow
pip install numpy
pip install scikit-learn
pip install matplotlib
pip install pandas
```

--------------------------------

### Initialize Data Structures and Parameters

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/SongGenreClassifier/SongGenreClassifier.ipynb

Sets up parameters and initializes DataFrames for training and testing the genre classifier.

```python
from sklearn.utils import shuffle
from nltk.corpus import stopwords
genres = ['Country','alt rock', 'Hip Hop','pop','rhythm and blues']
LYRIC_LEN = 400
N = 10000
RANDOM_SEED = 200
train_df = pd.DataFrame()
test_df = pd.DataFrame()
```

--------------------------------

### Count Unique Start Locations

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/UBER_DATA_ANALYSIS.ipynb

Determines the total number of distinct starting locations present in the 'START*' column of the DataFrame.

```python
len(df['START*'].unique())
```

--------------------------------

### Install specific pyyaml version

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Gender Pay Gap Analysis/analyze_gender_pay_gap.ipynb

Installs a specific version of the pyyaml library. This is often done to ensure compatibility with other installed packages.

```python
!pip install pyyaml==5.4.1
```

--------------------------------

### Tesseract Configuration File Example

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Dark Pattern Detection/Tesseract-OCR/tesseract.1.html

Example of a Tesseract configuration file that disables system dictionaries and loads user-defined word and pattern lists.

```text
load_system_dawg     F
load_freq_dawg       F
user_words_suffix    user-words
user_patterns_suffix user-patterns
```

--------------------------------

### Run Streamlit Application

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/emotion-based music recommendtion/README.md

Launches the Streamlit application for the emotion-based music recommender. This command starts the local development server.

```bash
streamlit run front.py
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Traffic Sign Recognition/Readme.md

Run this command to install all the necessary Python packages for the traffic sign recognition project. Ensure you have pip installed.

```bash
pip install pandas keras tensorflow matplotlib pillow sklearn
```

--------------------------------

### Install TPOT Package

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Statistical_modeling_python/Statistical_modeling.ipynb

This command installs the TPOT (Tree-based Pipeline Optimization Tool) library, which automates the process of building and optimizing machine learning pipelines.

```bash
pip install tpot

```

--------------------------------

### Example Usage of Text Summarization

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Text Summarization Model/Model/text_summary.ipynb

Demonstrates how to use the generate_summary function with a sample text and prints the resulting summary.

```python
# Example usage:
text = """A week ago a friend invited a couple of other couples over for dinner. Eventually, the food (but not the wine) was cleared off the table for what turned out to be some fierce Scrabbling. Heeding the strategy of going for the shorter, more valuable word over the longer cheaper word, our final play was “Bon,” which–as luck would have it!–happens to be a Japanese Buddhist festival, and not, as I had originally asserted while laying the tiles on the board, one half of a chocolate-covered cherry treat. Anyway, the strategy worked. My team only lost by 53 points instead of 58."""

summary = generate_summary(text)
print(summary)
```

--------------------------------

### Install Python Libraries

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Bitcoin Price Predictor/README.md

Installs necessary Python libraries for data manipulation, machine learning, and visualization. Ensure these are installed before running the project.

```bash
pip install sklearn
pip install tensorflow
pip install pandas
pip install matplotlib
```

--------------------------------

### TPOT Installation Output

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Statistical_modeling_python/Statistical_modeling.ipynb

This output details the process of installing the TPOT package and its dependencies using pip. It shows which packages are being collected, downloaded, and installed.

```text
Collecting tpot
  Downloading TPOT-0.12.2-py3-none-any.whl (87 kB)
⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒ 87.4/87.4 kB ⌒ 2.4 MB/s eta ⌒ 0:00:00
Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.25.2)
Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.11.4)
Requirement already satisfied: scikit-learn>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.4.2)
Collecting deap>=1.2 (from tpot)
  Downloading deap-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (135 kB)
⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒⌒ 135.4/135.4 kB ⌒ 5.9 MB/s eta ⌒ 0:00:00
Collecting update-checker>=0.16 (from tpot)
  Downloading update_checker-0.18.0-py3-none-any.whl (7.0 kB)
Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.10/dist-packages (from tpot) (4.66.4)
Collecting stopit>=1.1.1 (from tpot)
  Downloading stopit-1.1.2.tar.gz (18 kB)
  Preparing metadata (setup.py) ... ⌒ 25l⌒ 25hdone
Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from tpot) (2.0.3)
Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.3.2)
Requirement already satisfied: xgboost>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tpot) (2.0.3)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->tpot) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->tpot) (2023.4)
Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->tpot) (2024.1)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.4.1->tpot) (3.5.0)
Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from update-checker>=0.16->tpot) (2.31.0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=0.24.2->tpot) (1.16.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.7)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2024.6.2)
Building wheels for collected packages: stopit
  Building wheel for stopit (setup.py) ... ⌒ 25l⌒ 25hdone
  Created wheel for stopit: filename=stopit-1.1.2-py3-none-any.whl size=11938 sha256=d1f6477161421803d96ec931ee6b733f64adfb8adfa4fa91d4aa1eeb71891866
  Stored in directory: /root/.cache/pip/wheels/af/f9/87/bf5b3d565c2a007b4dae9d8142dccc85a9f164e517062dd519
Successfully built stopit

```

--------------------------------

### Install PyTorch for CPU

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/NLP-Project/README.md

Installs PyTorch version 1.12.1 with CPU support. This is a prerequisite for installing and running iNLTK, especially in environments without CUDA.

```bash
pip install torch==1.12.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Bitcoin Price Prediction Web App/requirements.txt

List of Python packages required for the Bitcoin Price Prediction Web App. Install these using pip.

```text
joblib==1.1.0
numpy==1.22.0
scikit-learn==1.0.2
scipy==1.7.3
threadpoolctl==3.0.0
```

--------------------------------

### Count Occurrences of Each Start Location

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/UBER_DATA_ANALYSIS.ipynb

Calculates and returns the frequency of each unique value in the 'START*' column. This helps identify the most common starting points for Uber rides.

```python
df['START*'].value_counts()
```

--------------------------------

### Install twitter-api-v2

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Social Media Fake Accounts Detection with Interactive UI/server/node_modules/twitter-api-v2/README.md

Install the library using yarn or npm.

```bash
yarn add twitter-api-v2
# or
npm i twitter-api-v2
```

--------------------------------

### Initialize Video Capture and MediaPipe Hands

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Sign-Language-Translator/code file sign language translator.ipynb

Sets up video capture from the default camera and initializes MediaPipe Hands for detecting up to two hands with specified confidence thresholds.

```python
cap = cv2.VideoCapture(0)
mphands = mp.solutions.hands
hands = mphands.Hands(static_image_mode=False,max_num_hands=2,
                      min_detection_confidence=0.5,
                      min_tracking_confidence=0.5)
mpDraw = mp.solutions.drawing_utils()

txt1 = "1"
txt2 = "2"
txt3 = "3"
txt4 = "4"
txt5 = "5"
txt6 = "6"
txt7 = "7"
txt8 = "8"
```

--------------------------------

### Install and Import Deployment Libraries

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Sentiment-Analysis/Sentiment_Analysis_Major_Project.ipynb

Installs the Streamlit and pyngrok libraries, then imports ngrok from pyngrok. Streamlit is used for building web applications, and pyngrok for creating secure tunnels.

```python
!pip install streamlit
!pip install pyngrok===4.1.1
from pyngrok import ngrok
```

--------------------------------

### Install PaddlePaddle GPU

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/OCR-Medicine-Reader/Medicine_Label_Reader_OCR.ipynb

Installs the paddlepaddle-gpu package version 2.0.0 from a Baidu mirror. Ensure you have the necessary GPU drivers and CUDA toolkit installed for GPU acceleration.

```python
# Installation of paddle github repository 
!python -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
```

--------------------------------

### Initialize YouTube API and Emotion Detection Model

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/emotion-based music recommendtion/work.ipynb

Sets up the necessary libraries and loads the pre-trained emotion detection model. Ensure you replace 'your-api' with your actual YouTube API key.

```python
import googleapiclient.discovery
from transformers import pipeline

# YouTube API credentials
api_key = 'your-api'

# Load pre-trained emotion detection model
emotion_classifier = pipeline('text-classification', model='bhadresh-savani/bert-base-go-emotion')
```

--------------------------------

### Install Docker on EC2

Source: https://github.com/niketkumardheeryan/ml-capsule/blob/master/Chicken_Disease_Classification/README.md

Commands to install Docker on an Ubuntu EC2 instance. This includes updating packages, downloading the Docker installation script, and adding the current user to the docker group.

```bash
sudo apt-get update -y
sudo apt-get upgrade
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ubuntu
newgrp docker
```