Who We Are?

Our Mission

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
fat = [23,21,20,12,13,15]
carbs = [34,32,21,43,23,12]
plt.scatter(x,y)
plt.show()
x = np.array(fat).reshape(-1,1)
y = np.array(carbs)
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x,y)
y_pred = model.predict(x)
plt.scatter(x,y)
plt.plot(x,y_pred)
plt.show()
print(“slope:” , model.coef_[0])
print(“intercept:” , model.intercept_)

digital developer

Extraordinary Experiences

pip install pandas

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error,r2_score

data = { ‘bedrooms’:[3,4,2,5,3],
‘bathrooms’:[2,3,1,4,2],
‘sqft’:[1200,1500,800,2000,1000],
‘price’:[250000,300000,180000,400000,220000]}
df= pd.DataFrame(data)

X = df[[‘bedrooms’,’bathrooms’,’sqft’]]

Y = df[‘price’]

X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=42)

model=LinearRegression()

model.fit(X_train,Y_train)

Y_pred=model.predict(X_test)

mse=mean_squared_error(Y_test,Y_pred)

r2=r2_score(Y_test,Y_pred)

print(“MSE”,mse)

print(“coefficients”,model.coef_)

print(“intercept”,model.intercept_)

print(“MSE”,mse)

print(“r_squared”,r2)

 

 

Our Core Values

  • Innovation – Embracing the latest tech to build modern solutions.
  • User Focus – Designing with the end-user in mind.
  • Quality – Clean code, pixel-perfect execution.
  • Collaboration – Open, honest, and team-driven.
  • Performance – Fast, scalable, and reliable builds.
  • Security – Safe, secure, and trustworthy systems.
  • Growth – Always learning, always improving.
  • Creativity – Blending art with functionality.