Python डेटा विज्ञान/आईरिस डेटासेट रिपोर्ट

Internship Report Summary

• Project Title: Python For Data Science/ Iris Dataset
• Student: Pooja
• Roll Number: 2823671
• Batch: 2023-2027
• Institution: Panipat Institute of Engineering & Technology
• University: Kurukshetra University
• Duration: 8 July to 16 August
• Supervisor: Miss Shivangi Goyal
• Objective: Applying Python for data analysis and modeling on the Iris dataset, predicting species based on features.

Data Science/Machine Learning Project Overview

• Dataset: Iris Dataset, used to demonstrate machine learning techniques.
• Features: Sepal length, sepal width, petal length, petal width
• Species: Setosa, Versicolor, Virginica (3 types)
• Process:
  • Exploratory Data Analysis (EDA) to understand data patterns.
  • Data cleaning, handling missing values, and outliers.
  • Feature selection and scaling.
  • Machine learning algorithms (KNN, Random Forest, Regression) used.
  • Evaluation via Accuracy scores, confusion matrices and cross-validation.

Key Concepts

• Exploratory Data Analysis (EDA): Initial stage to understand dataset patterns and relationships.
• Machine Learning Algorithms: Used to classify species via their features
• K-Nearest Neighbors (KNN): Classification algorithm.
• Random Forest Classifier: Classification algorithm.
• Regression Model: Applicable based on target variable.
• Data Preprocessing: Data cleaning, handling missing values & outliers, feature scaling are part of this process.

Software and Tools

• Python Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
• Programming Language: Python (used for the data science project)

Graphic Representations

• Correlation Heatmap (Graph 5.1.1) used to visualize variable relationships.
• Histograms (Graph 5.1.2) represent the distribution of data.
• Scatter plots (Graph 5.1.3) for visualizing relationships between variables.

Technical terms used

Python, Data Science, Machine Learning, Iris Dataset, KNN, Random Forest classification, EDA, Data analysis, Correlation, Heatmap, Histogram, Scatter plot, Regression model.

इस इंटरनशिप रिपोर्ट में Python का उपयोग करते हुए डेटा विज्ञान पर आधारित परियोजना का सारांश प्रस्तुत किया गया है। इसमें आईरिस डेटासेट का विश्लेषण और प्रजातियों की भविष्यवाणी के लिए मशीन लर्निंग तकनीकों की व्याख्या की गई है। परियोजना का उद्देश्य डेटा साफ करने, विशेषताओं का चयन और विभिन्न मशीन लर्निंग एल्गोरिदम का उपयोग करना है।