Hi there! I'm Naila Rais, a passionate Data Analyst from Aligarh, India. I specialize in making sense of complex datasets and transforming them into actionable insights for data-driven decision-making. Whether it's predictive modeling, data visualization, or automation, I'm always excited to push the boundaries of data science.

DataCamp Certified Associate Data Analyst with a strong foundation in data analysis, data cleaning, and visualization techniques. I am proficient in tools like Python, SQL, Power BI, Tableau, and Excel. My passion lies in leveraging data to uncover hidden patterns and trends, helping businesses thrive.

• 📊 Developed predictive models using Machine Learning for real estate valuation, marketing, and finance industries.
• ⚡ Automated data processing and reporting, reducing time spent on manual tasks by 70%.
• 📈 Created interactive dashboards with Power BI and Tableau, enabling real-time monitoring of business KPIs.
• 🚀 Contributed to a project at Prodigy InfoTech that boosted customer engagement by 15% through a recommendation system.
• 🌍 Led a data migration project, ensuring seamless transition from legacy systems to cloud-based infrastructure.

Description: Built a comprehensive platform using Python and Azure ML Studio to forecast sales trends for a major retail chain.
Repository: Predictive-Analytics-Platform

Description: Designed a K-Means clustering model to identify customer segments for targeted marketing campaigns.
Repository: Customer-Segmentation-ML

Description: Created a solution integrating IoT sensors and machine learning to optimize water usage in agricultural settings.
Repository: Water-Management-Optimization

Project: Developed a model to predict housing prices using multiple regression techniques.

import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns

# Load data
data = pd.read_csv('housing_data.csv')
features = data[['Square_Feet', 'Bedrooms', 'Bathrooms']]
target = data['Price']

# Split data
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# Train model
model = RandomForestRegressor()
model.fit(X_train, y_train)

# Predict and visualize
predictions = model.predict(X_test)
sns.scatterplot(x=y_test, y=predictions)
plt.xlabel('Actual Prices')
plt.ylabel('Predicted Prices')
plt.title('Housing Price Prediction')
plt.show()

Let's connect! I’m open to collaborations, new opportunities, or just a friendly chat.

• Email: naila.rais@sinclair.edu
• LinkedIn: Naila Rais
• Website: Pwed Club

I'm an avid coffee lover who believes in the magic of blending data and storytelling to craft compelling insights!

Thank you for visiting! ✨ Let's make data meaningful and fun together. 🚀