National Technology Day 2k23

Kamya123 — Thu, 11 May 2023 18:30:30 +0000

History

The significance of the 11th May in Indian History is when India achieved a major milestone in its technological advancements by conducting a series of successful nuclear tests. These tests codenamed "Operation Shakti," were conducted in the Pokhran region of Rajasthan under the leadership of then-Prime Minister "Shri Atal Bihari Vajpayee Ji". Since then, May 11 is being celebrated as a National Technology Day to acknowledge the contribution of scientists and engineers in the development of the country.

Indian Technology that Shaped the World

In recent years, India has emerged as a global tech hub, making significant contributions to the technology industry. From pioneering fiber optics to developing high-quality speakers and sound systems, Indian talents have impacted various fields such as healthcare, communications, space exploration, and personal computing. Let's take a closer look at some of these groundbreaking inventions that have made a significant impact on the world.

USB Drive

Indian-Americans Ajay Bhatt and Shabeer Bhatia are among the inventors who have revolutionized personal computing and communication. Ajay Bhatt is the man behind the ubiquitous USB drive, which has become a ubiquitous storage device. Sabeer Bhatia, on the other hand, co-founded Hotmail, the first web-based email service that was later acquired by Microsoft. Vinod Dham, another Indian-American, played a key role in the development of the Pentium chip, which revolutionized personal computing.

Fiber Optics

Indian Physicist Narinder Singh Kapany is another notable figure who pioneered the use of fiber optics in communications technology. His innovative work laid the foundation of modern fiber optic communication systems, which have transformed the way we communicate. Amar Bose, an Indian-American, founded Bose Corporation and developed high-quality speakers and sound systems that are renowned worldwide.

Gene Editing Technology

The impact of Indian inventions extends beyond computing and communication. Indian-American biochemist Jennifer Dounda co-invented CRISPR gene editing technology, which has the potential to revolutionize medicine. Indian-American Congressman Dalip Singh Saund introduced the H1-B visa program, which has allowed many skilled workers to come to the United States. A team of Indian scientists also developed Chaak, a low-cost, portable device that can detect cervical cancer.

Satellite

Finally, India's space program owes its success to the launch of Aryabhatta, the country's first satellite, in 1975. This groundbreaking invention paved the way for India's space program and helped to establish the country as a major player in the field of space exploration.

These inventions contributed significantly to the advancement of technology. On Technology Day 2k23, let us celebrate the Indian Inventors who have made a mark on the world and continue to inspire future generations.

Running Random Forest

Kamya123 — Sun, 06 Nov 2022 18:56:59 +0000

Introduction:

Random Forest is a Supervised Machine Learning Algorithm that is used widely in Classification and Regression problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression.
One of the most important features of the Random Forest Algorithm is that it can handle the data set containing continuous variables as in the case of regression and categorical variables as in the case of classification. It performs better results for classification problems.

Real Life Analogy:

Working of Random Forest Algorithm:

We need to know the Ensemble technique. Ensemble uses two types of methods:

1. Bagging - It creates a different training subset from the sample training data with a replacement & the final output is based on majority voting. For example, Random Forest.

2. Boosting - It combines weak learners into strong learners by creating sequential models such that the final model has the highest accuracy. For example, ADA BOOS, XG BOOST.

Steps involved in Random Forest:

Step 1: In Random Forest n number of random records is taken from the data set having k number of records.
Step 2: Individual decision trees are constructed for each sample.
Step 3: Each decision tree will generate an output.
Step 4: Final Output is considered based on the Majority Voting for Classification and regression respectively.

Coding in Python:

1. Let's Import the Libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

2. Importing data set:

df = pd.read_csv('heart_v2.csv')
print(df.head())
sns.countplot(df['heart disease'])
plt.title('Value counts of heart disease patients')
plt.show()

3. Putting Feature Variable to X and Target Variable to Y:

X = df.drop('heart disease',axis=1)
y = df['heart disease']

4. Train Test Split is Performed:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=42)
X_train.shape, X_test.shape

3. Import RandomForestClassifier and fit the data:

from sklearn.ensemble import RandomForestClassifier
classifier_rf = RandomForestClassifier(random_state=42, n_jobs=-1, max_depth=5, n_estimators=100, oob_score=True)
%%time
classifier_rf.fit(X_train, y_train)
classifier_rf.oob_score_

4. Hyperparameter tuning for Random Forest using GridSearchCV and fit the data:

rf = RandomForestClassifier(random_state=42, n_jobs=-1)
params = {
    'max_depth': [2,3,5,10,20],
    'min_samples_leaf': [5,10,20,50,100,200],
    'n_estimators': [10,25,30,50,100,200]
}
from sklearn.model_selection import GridSearchCV
grid_search = GridSearchCV(estimator=rf, param_grid=params, cv = 4, n_jobs=-1, verbose=1, scoring="accuracy")
%%time
grid_search.fit(X_train, y_train)

grid_search.best_score_

rf_best = grid_search.best_estimator_
rf_best

5. Visualization:

from sklearn.tree import plot_tree
plt.figure(figsize=(80,40))
plot_tree(rf_best.estimators_[5], feature_names = X.columns,class_names=['Disease', "No Disease"],filled=True);

from sklearn.tree import plot_tree
plt.figure(figsize=(80,40))
plot_tree(rf_best.estimators_[7], feature_names = X.columns,class_names=['Disease', "No Disease"],filled=True);

6. Sorting of Data according to feature importance:

rf_best.feature_importances_

imp_df = pd.DataFrame({
    "Varname": X_train.columns,
    "Imp": rf_best.feature_importances_
})

imp_df.sort_values(by="Imp", ascending=False)

Summary:

Now, we can conclude that Random Forest is one of the best techniques with high performance which is widely used in various industries for its efficiency. It can handle binary, continuous, and categorical data.
Random Forest is a great choice if anyone wants to build the model fast and efficiently as one of the best things about the random forest is it can handle missing values too.
Overall, Random Forest is fast, simple, flexible and robust model with some limitations.

DEV Community: Kamya123