Ascend
to Digital
Success
At the nexus of design and technology, we craft digital experiences, empowering our clients to envision tomorrow, now!
Innovative Solutions

Web & Mobile
Development
Digital
Marketing
Media &
Creative
Digetal.
Consulting
Digital Marketing
& Social Media
GROW YOUR
-
Reach
-
Impact
-
Leads
- Reach
- Impact
- Leads
Unlock the full potential of your brand with our expert social media strategies. We help you engage with your audience, grow your reach, and drive impactful results

Web & Mobile Solutions
Scale With Your Business Growth
We plan every project with scalability and flexibility in mind to ensure our proposed solutions can service your needs for years to come and adapt to shifting business dynamics with minimal interruption, effort, and cost.
# Importing necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
# Load dataset
url = 'https://example.com/dataset.csv'
data = pd.read_csv(url)
# Display basic information about the dataset
print("Dataset Information:")
print(data.info())
# Display first few rows of the dataset
print("\nFirst 5 rows of the dataset:")
print(data.head())
# Check for missing values
print("\nMissing values in the dataset:")
print(data.isnull().sum())
# Handling missing values by filling them with the mean of the column
data.fillna(data.mean(), inplace=True)
# Exploratory Data Analysis (EDA)
plt.figure(figsize=(10, 6))
sns.heatmap(data.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()
# Distribution of target variable
plt.figure(figsize=(10, 6))
sns.histplot(data['target_variable'], kde=True)
plt.title('Distribution of Target Variable')
plt.show()
# Splitting the dataset into features and target variable
X = data.drop('target_variable', axis=1)
y = data['target_variable']
# Splitting the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Feature scaling
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Training a Linear Regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Making predictions
y_pred = model.predict(X_test)
# Evaluating the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"\nModel Performance:\nMean Squared Error: {mse}\nR-Squared: {r2}")
# Visualizing the actual vs predicted values
plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred, color='blue')
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title('Actual vs Predicted Values')
plt.show()
# Conclusion
print("\nConclusion:")
print("The Linear Regression model has been trained and evaluated on the sample dataset.")
print("Further improvements can be made by experimenting with different models and feature engineering techniques.")
Contact Us
We’re just one click away to help you to craft the right digital solution for your business or brand. Fill in the form to share more details about your project.
-
7380 W Sand Lake Rd. #500
Orlando, FL 32819 - [email protected]
- +1 202 670 0064