Update pages/Intro.py

pages/Intro.py

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+import streamlit as st
+import matplotlib.pyplot as plt
+import pandas as  pd
+import plotly.graph_objects as go
+
+image_path_1 = 'C:/Users/DELL E5490/Desktop/project/data/.env/DataAnalysis.jpg'
+image_path_2 = 'C:/Users\DELL E5490\Desktop\project\data\.env\Data-Analytics-Life-Cycle.jpg'
+st.image(image_path_1,caption=None, width=int, use_column_width=True, clamp=True, channels="RGB", output_format="auto")
+
+st.title("Steps to get Intution of Data Analysis")
+
+st.header("What is Data 📊?")
+st.markdown("Data consists of facts and information that are used to generate insights and conclusions.")
+
+st.header("What is Data Analysi📈?")
+st.markdown("""
+Data analysis is a comprehensive process that involves collecting, cleaning, transforming, integrating, reducing, and validating data to uncover meaningful insights. Python is a versatile and powerful tool for data analysis due to its extensive libraries, automation capabilities, and strong community support. By following a structured roadmap for data analysis, including univariate, bivariate, and multivariate analyses, you can effectively explore and understand your data to make informed decisions.""")
+
+
+st.header("Why Use Python for Data Analysis 🤔?")
+
+st.markdown("""
+While tools like Excel and Power BI are commonly used for data visualization and analysis, Python is a powerful language for data analysis due to several reasons:
+
+- *Versatility*: Python can handle a wide variety of data types and sources, making it suitable for diverse data analysis tasks.
+- *Libraries*: Python has extensive libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn that facilitate data manipulation, analysis, and visualization.
+- *Automation*: Python allows for automation of repetitive tasks and complex computations, improving efficiency and productivity.
+- *Community Support*: Python has a large and active community which means abundant resources, tutorials, and support.
+""")
+
+advantages = ['Versatility', 'Libraries', 'Automation', 'Community Support']
+scores = [8, 9, 7, 9] 
+
+fig, ax = plt.subplots()
+ax.barh(advantages, scores, color=['blue', 'green', 'orange', 'red'])
+ax.set_xlabel('Importance')
+ax.set_title('Advantages of Python for Data Analysis')
+
+st.pyplot(fig)
+
+st.header("Data Analysis Life Cycle🔄")
+
+st.markdown("""
+The data analysis life cycle includes the following steps:
+
+- **Case Scenario**: Define the problem statement and the objectives of the analysis.
+- **Collect the Data**: If no data is available, fetch the data from relevant sources.
+- **Data Understanding**: Preprocess the data by understanding its shape, data types, and description.
+- **Data Cleaning**: Clean the null values in the data using imputation methods to ensure accurate analysis.
+- **Data Visualization**: Understand the underlying patterns of the data through univariate and bivariate analyses.
+- **Outlier Detection**: Remove outliers that create biases in the results.
+- **Data Transformation**: Apply transformations for better understanding and analysis of the data.
+""")