@LEARNEVERYTHINGAI
DISCOVER THE
MAGIC BEHIND
DATA ANALYSIS AND
THE CODE THAT
MAKES IT POSSIBLE!
SHIVAM MODI
@learneverythingai
@LEARNEVERYTHINGAI
DATA COLLECTION
import pandas as pd
# Load data from a CSV file
data = pd.read_csv('data.csv')
# Explore the data
print(data.head())
The first step is to collect the data. Here, we're loading data from a
CSV file using Python's pandas library.
SHIVAM MODI
@learneverythingai
@LEARNEVERYTHINGAI
DATA CLEANING
# Remove missing values
data = data.dropna()
# Convert data types
data['date'] = pd.to_datetime(data['date'])
# Handle outliers
data = data[data['value'] < 1000]
After collecting the data, it's important to clean it. This code snippet
demonstrates removing missing values, converting data types, and
handling outliers.
SHIVAM MODI
@learneverythingai
@LEARNEVERYTHINGAI
EXPLORATORY DATA ANALYSIS
import matplotlib.pyplot as plt
# Visualize data distribution
plt.hist(data['value'], bins=10)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()
Exploratory Data Analysis (EDA) helps us understand the data. This
code generates a histogram to visualize the distribution of a specific
variable.
SHIVAM MODI
@learneverythingai
@LEARNEVERYTHINGAI
STATISTICAL ANALYSIS
import numpy as np
# Calculate mean and standard deviation
mean_value = np.mean(data['value'])
std_value = np.std(data['value'])
# Print the results
print("Mean:", mean_value)
print("Standard Deviation:", std_value)
Statistical analysis allows us to derive meaningful insights from data.
Here, we calculate the mean and standard deviation using NumPy.
SHIVAM MODI
@learneverythingai
@LEARNEVERYTHINGAI
DATA VISUALIZATION
# Plot a line chart
plt.plot(data['date'], data['value'])
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Value over Time')
plt.show()
Data visualization helps us present our findings effectively. This code
snippet generates a line chart to visualize the trend of a variable over
time.
SHIVAM MODI
@learneverythingai
@LEARNEVERYTHINGAI
PREDICTIVE MODELING
from sklearn.linear_model import LinearRegression
# Prepare data for modeling
X = data[['date']]
y = data['value']
# Train a linear regression model
model = LinearRegression()
model.fit(X, y)
Predictive modeling allows us to forecast future outcomes. Here, we
train a linear regression model using scikit-learn to predict values
based on dates.
SHIVAM MODI
@learneverythingai
@LEARNEVERYTHINGAI
COMMUNICATE INSIGHTS
# Make predictions
predictions = model.predict(X)
# Visualize predicted values
plt.plot(data['date'], data['value'], label='Actual')
plt.plot(data['date'], predictions, label='Predicted')
plt.xlabel('Date')
plt.ylabel('Value')
plt.legend()
plt.show()
Finally, we communicate our insights. This code snippet visualizes
the actual values and the predicted values from our model.
SHIVAM MODI
@learneverythingai
@learneverythingai
Like this Post?
Follow Me
Share with your friends
Check out my previous posts
SAVE THIS
SHIVAM MODI
@learneverythingai
www.learneverythingai.com
