├── PandasAI-Agent ├── requirements.txt ├── charts │ ├── chart-1.png │ ├── chart-2.png │ ├── chart-3.png │ ├── chart-4.png │ ├── chart-5.png │ ├── chart-6.png │ ├── chart-7.png │ └── chart-8.png ├── app.py └── E-commerce-Customer-Behavior.csv ├── MySQL-with-Ollama ├── requirements.txt ├── app.py └── customer.csv ├── PandasAI-with-Llama3 ├── requirements.txt ├── charts │ ├── heatmap.png │ ├── fare-by-sex.png │ ├── pclass-pie.png │ ├── age-histogram.png │ ├── fare-distribution.png │ └── sex-distribution.png └── app.py ├── Data-Analysis-with-GPT-4o ├── requirements.txt ├── data │ ├── pandasai.png │ └── penguins.csv └── app.py ├── Images └── pandaai-5.png ├── PandasAI-with-Ollama ├── requirements.txt ├── exports │ └── charts │ │ └── temp_chart.png ├── app.py └── iris.csv ├── Data-Visualization-with-PandasAI ├── requirements.txt ├── exports │ └── charts │ │ └── temp_chart.png └── app.py ├── Datasets ├── population.csv ├── E-commerce-Customer-Behavior.csv └── titanic.csv ├── .github └── workflows │ └── main.yml ├── README.md ├── Pandas-AI-with-Ollama.ipynb └── pandasai.ipynb /PandasAI-Agent/requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | pandasai 3 | streamlit -------------------------------------------------------------------------------- /MySQL-with-Ollama/requirements.txt: -------------------------------------------------------------------------------- 1 | pandasai[connectors] 2 | streamlit -------------------------------------------------------------------------------- /PandasAI-with-Llama3/requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | pandasai 3 | streamlit -------------------------------------------------------------------------------- /Data-Analysis-with-GPT-4o/requirements.txt: -------------------------------------------------------------------------------- 1 | pandasai 2 | langchain_openai 3 | streamlit 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Brazil,216422446 12 | Bangladesh,172954319 13 | Russia,144444359 14 | Mexico,128455567 15 | Ethiopia,126527060 16 | Philippines,117337368 17 | Egypt,112716598 18 | DR Congo,102262808 19 | Vietnam,98858950 20 | Iran,89172767 21 | Thailand,71801279 22 | -------------------------------------------------------------------------------- /.github/workflows/main.yml: -------------------------------------------------------------------------------- 1 | name: Latest YouTube Videos 2 | on: 3 | schedule: 4 | # Runs every hour, on the hour 5 | - cron: "0 0 * * *" 6 | workflow_dispatch: 7 | 8 | jobs: 9 | update-readme-with-youtube: 10 | name: Update this repo's README with latest videos from YouTube 11 | runs-on: ubuntu-latest 12 | steps: 13 | - uses: actions/checkout@v2 14 | - uses: gautamkrishnar/blog-post-workflow@master 15 | with: 16 | comment_tag_name: "YOUTUBE" 17 | feed_list: "https://www.youtube.com/playlist?list=PLbQRubTta6fep1BSGsSOmuYtu4ncNGnG8" 18 | -------------------------------------------------------------------------------- /Data-Visualization-with-PandasAI/app.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | import streamlit as st 3 | import seaborn as sns 4 | from langchain_anthropic import ChatAnthropic 5 | from pandasai import SmartDataframe 6 | 7 | load_dotenv() 8 | 9 | st.title("Data visualization with PandasAI") 10 | 11 | data = sns.load_dataset("penguins") 12 | st.write(data.head(3)) 13 | 14 | model = ChatAnthropic(model="claude-3-haiku-20240307") 15 | df = SmartDataframe(data, config={"llm": model}) 16 | 17 | prompt = st.text_area("Enter your prompt:") 18 | 19 | if st.button("Generate:"): 20 | if prompt: 21 | with st.spinner("Generating response..."): 22 | st.write(df.chat(prompt)) -------------------------------------------------------------------------------- /PandasAI-with-Llama3/app.py: -------------------------------------------------------------------------------- 1 | from pandasai.llm.local_llm import LocalLLM 2 | import streamlit as st 3 | import pandas as pd 4 | from pandasai import SmartDataframe 5 | 6 | model = LocalLLM( 7 | api_base="http://localhost:11434/v1", 8 | model="llama3" 9 | ) 10 | 11 | st.title("Data analysis with PandasAI") 12 | 13 | uploaded_file = st.file_uploader("Upload a CSV file", type=['csv']) 14 | 15 | if uploaded_file is not None: 16 | data = pd.read_csv(uploaded_file) 17 | st.write(data.head(3)) 18 | 19 | df = SmartDataframe(data, config={"llm": model}) 20 | prompt = st.text_area("Enter your prompt:") 21 | 22 | if st.button("Generate"): 23 | if prompt: 24 | with st.spinner("Generating response..."): 25 | st.write(df.chat(prompt)) -------------------------------------------------------------------------------- /PandasAI-with-Ollama/app.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from langchain_community.llms import Ollama 3 | import pandas as pd 4 | from pandasai import SmartDataframe 5 | 6 | llm = Ollama(model="mixtral") 7 | 8 | st.title("Data Analysis with PandasAI") 9 | 10 | uploader_file = st.file_uploader("Upload a CSV file", type= ["csv"]) 11 | 12 | if uploader_file is not None: 13 | data = pd.read_csv(uploader_file) 14 | st.write(data.head(3)) 15 | df = SmartDataframe(data, config={"llm": llm}) 16 | prompt = st.text_area("Enter your prompt:") 17 | 18 | if st.button("Generate"): 19 | if prompt: 20 | with st.spinner("Generating response..."): 21 | st.write(df.chat(prompt)) 22 | else: 23 | st.warning("Please enter a prompt!") -------------------------------------------------------------------------------- /PandasAI-Agent/app.py: -------------------------------------------------------------------------------- 1 | from pandasai.llm.local_llm import LocalLLM 2 | import streamlit as st 3 | import pandas as pd 4 | from pandasai import Agent 5 | 6 | model = LocalLLM( 7 | api_base="http://localhost:11434/v1", 8 | model="llama3" 9 | ) 10 | 11 | st.title("Data analysis with PandasAI Agent") 12 | 13 | uploaded_file = st.sidebar.file_uploader( 14 | "Upload a CSV file", 15 | type=["csv"] 16 | ) 17 | 18 | if uploaded_file is not None: 19 | data = pd.read_csv(uploaded_file) 20 | st.write(data.head(3)) 21 | 22 | agent = Agent(data, config={"llm": model}) 23 | prompt = st.text_input("Enter your prompt:") 24 | 25 | if st.button("Generate"): 26 | if prompt: 27 | with st.spinner("Generating response..."): 28 | st.write(agent.chat(prompt)) 29 | 30 | -------------------------------------------------------------------------------- /MySQL-with-Ollama/app.py: -------------------------------------------------------------------------------- 1 | from pandasai.llm.local_llm import LocalLLM 2 | import streamlit as st 3 | from pandasai.connectors import MySQLConnector 4 | from pandasai import SmartDataframe 5 | 6 | my_connector = MySQLConnector( 7 | config={ 8 | "host":"localhost", 9 | "port":3306, 10 | "database":"customer_behavior", 11 | "username":"root", 12 | "password":"1234", 13 | "table":"customer", 14 | } 15 | ) 16 | 17 | model = LocalLLM( 18 | api_base="http://localhost:11434/v1", 19 | model="llama3" 20 | ) 21 | 22 | df_connector = SmartDataframe(my_connector, config={"llm": model}) 23 | 24 | st.title("MySQL with Llama-3") 25 | 26 | prompt = st.text_input("Enter your prompt:") 27 | 28 | if st.button("Generate"): 29 | if prompt: 30 | with st.spinner("Generating response..."): 31 | st.write(df_connector.chat(prompt)) 32 | 33 | 34 | -------------------------------------------------------------------------------- /Data-Analysis-with-GPT-4o/app.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | import pandas as pd 3 | from langchain_openai import ChatOpenAI 4 | from pandasai import SmartDataframe 5 | 6 | st.set_page_config(page_title="Talk to Your Data") 7 | st.title("Talk to Your Data 🐼") 8 | 9 | st.sidebar.image("data/pandasai.png", use_column_width=True) 10 | 11 | uploaded_file = st.sidebar.file_uploader("Upload your CSV file", type=["csv"]) 12 | 13 | if uploaded_file is not None: 14 | data = pd.read_csv(uploaded_file) 15 | st.write(data.head(3)) 16 | 17 | openai_api_key = st.sidebar.text_input("OpenAI API Key", type = "password", 18 | disabled=not (uploaded_file)) 19 | 20 | if not openai_api_key.startswith("sk-"): 21 | st.warning("Please enter your OpenAI API key!", icon="⚠️") 22 | 23 | prompt = st.text_input("Enter your prompt") 24 | 25 | def generate_response(csv_file, prompt): 26 | llm = ChatOpenAI(model="gpt-4o-2024-05-13", openai_api_key=openai_api_key, 27 | temperature=0) 28 | df = SmartDataframe(data, config={"llm":llm}) 29 | response = df.chat(prompt) 30 | return st.write(response) 31 | 32 | if st.button("Ask your data"): 33 | if openai_api_key.startswith("sk-") and (uploaded_file is not None): 34 | if prompt: 35 | with st.spinner("Generating response..."): 36 | generate_response(uploaded_file, prompt) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Learn PandasAI with Examples 2 | 3 | [![](https://img.shields.io/badge/python-darkblue?&style=plastic&logo=python&logoColor=white)]() 4 | [![](https://img.shields.io/badge/pandasai-darkred?&style=plastic&logo=pandas&logoColor=white)]() 5 | [![](https://img.shields.io/badge/ollama-black?&style=plastic&logo=ollama&logoColor=white)]() 6 | [![](https://img.shields.io/badge/streamlit-darkgreen?&style=plastic&logo=streamlit&logoColor=white)]() 7 | [![](https://img.shields.io/badge/chainlit-blue?&style=plastic&logo=chainlit&logoColor=white)]() 8 | [![](https://img.shields.io/badge/llama3-640D6B?&style=plastic&logo=meta&logoColor=white)]() 9 | 10 | Welcome to my PandasAI repo. This repo includes tutorials on how to use Pandas AI. Let me briefly explain this tool. 11 | 12 | [PandasAI](https://docs.pandas-ai.com/en/latest/) is an amazing Python library that allows you to talk to your data. It helps you to explore, clean, and analyze your data using generative AI. 13 | 14 | ### Features: 15 | 16 | - Ask questions about your data in natural language. 17 | - Generate plots to visualize your data. 18 | - Clean datasets by addressing missing values. 19 | - Enhance data quality through feature generation. 20 | - Connect to various data sources like CSV, XLSX, PostgreSQL, MySQL, BigQuery, Databrick, Snowflake, etc. 21 | 22 | To install PandasAI, run this command: 23 | 24 | ``` 25 | # Using poetry (recommended) 26 | poetry add pandasai 27 | 28 | # Using pip 29 | pip install pandasai 30 | ``` 31 | 32 | 33 | 34 | 35 | 36 | ### 🚀 My YouTube Videos (Python code walkthrough) 👇 37 | 38 | IMAGE ALT TEXT HERE 39 | 40 | - [MySQL with PandasAI](https://youtu.be/o88et_D8qlg) 41 | - [PandasAI Agent](https://youtu.be/9nyiePIrtbE) 42 | - [PandasAI with Llama-3](https://youtu.be/_dDaNgBDoHY) 43 | - [Data visualization with PandasAI](https://youtu.be/j-FQnJvesH4) 44 | - [Build an app with PandasAI, Ollama and Streamlit](https://youtu.be/-bt9grGmNvs) 45 | - [Pandas with Groq API](https://youtu.be/C6R9JLHZDH0) 46 | - [Data Analysis with PandasAI and Ollama](https://youtu.be/bw_e6xgGSTY) 47 | - [Introduction to Pandas AI](https://youtu.be/aUds2W7A_FY) 48 | 49 | ### 🚀 Medium 50 | - [Meet PandasAI](https://levelup.gitconnected.com/pandasai-unlocking-the-power-of-data-with-generative-ai-3196cbccba34) 51 | 52 | 🔗 Let's connect [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [X](http://x.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) 😎 53 | -------------------------------------------------------------------------------- /PandasAI-with-Ollama/iris.csv: -------------------------------------------------------------------------------- 1 | sepal_length,sepal_width,petal_length,petal_width,species 2 | 5.1,3.5,1.4,0.2,setosa 3 | 4.9,3.0,1.4,0.2,setosa 4 | 4.7,3.2,1.3,0.2,setosa 5 | 4.6,3.1,1.5,0.2,setosa 6 | 5.0,3.6,1.4,0.2,setosa 7 | 5.4,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5.0,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | 4.9,3.1,1.5,0.1,setosa 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3.0,1.4,0.1,setosa 15 | 4.3,3.0,1.1,0.1,setosa 16 | 5.8,4.0,1.2,0.2,setosa 17 | 5.7,4.4,1.5,0.4,setosa 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | 5.4,3.4,1.7,0.2,setosa 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1.0,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5.0,3.0,1.6,0.2,setosa 28 | 5.0,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.1,setosa 37 | 5.0,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.1,1.5,0.1,setosa 40 | 4.4,3.0,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5.0,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5.0,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3.0,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5.0,3.3,1.4,0.2,setosa 52 | 7.0,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4.0,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1.0,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5.0,2.0,3.5,1.0,versicolor 63 | 5.9,3.0,4.2,1.5,versicolor 64 | 6.0,2.2,4.0,1.0,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3.0,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1.0,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4.0,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3.0,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3.0,5.0,1.7,versicolor 80 | 6.0,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1.0,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1.0,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6.0,2.7,5.1,1.6,versicolor 86 | 5.4,3.0,4.5,1.5,versicolor 87 | 6.0,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3.0,4.1,1.3,versicolor 91 | 5.5,2.5,4.0,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3.0,4.6,1.4,versicolor 94 | 5.8,2.6,4.0,1.2,versicolor 95 | 5.0,2.3,3.3,1.0,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3.0,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3.0,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6.0,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3.0,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3.0,5.8,2.2,virginica 107 | 7.6,3.0,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2.0,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3.0,5.5,2.1,virginica 115 | 5.7,2.5,5.0,2.0,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3.0,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6.0,2.2,5.0,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2.0,virginica 124 | 7.7,2.8,6.7,2.0,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6.0,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3.0,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3.0,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2.0,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3.0,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6.0,3.0,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3.0,5.2,2.3,virginica 148 | 6.3,2.5,5.0,1.9,virginica 149 | 6.5,3.0,5.2,2.0,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3.0,5.1,1.8,virginica -------------------------------------------------------------------------------- /Data-Analysis-with-GPT-4o/data/penguins.csv: -------------------------------------------------------------------------------- 1 | species,island,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g,sex 2 | Adelie,Torgersen,39.1,18.7,181,3750,MALE 3 | Adelie,Torgersen,39.5,17.4,186,3800,FEMALE 4 | Adelie,Torgersen,40.3,18,195,3250,FEMALE 5 | Adelie,Torgersen,NA,NA,NA,NA,NA 6 | Adelie,Torgersen,36.7,19.3,193,3450,FEMALE 7 | Adelie,Torgersen,39.3,20.6,190,3650,MALE 8 | Adelie,Torgersen,38.9,17.8,181,3625,FEMALE 9 | Adelie,Torgersen,39.2,19.6,195,4675,MALE 10 | Adelie,Torgersen,34.1,18.1,193,3475,NA 11 | Adelie,Torgersen,42,20.2,190,4250,NA 12 | Adelie,Torgersen,37.8,17.1,186,3300,NA 13 | Adelie,Torgersen,37.8,17.3,180,3700,NA 14 | Adelie,Torgersen,41.1,17.6,182,3200,FEMALE 15 | Adelie,Torgersen,38.6,21.2,191,3800,MALE 16 | Adelie,Torgersen,34.6,21.1,198,4400,MALE 17 | Adelie,Torgersen,36.6,17.8,185,3700,FEMALE 18 | Adelie,Torgersen,38.7,19,195,3450,FEMALE 19 | Adelie,Torgersen,42.5,20.7,197,4500,MALE 20 | Adelie,Torgersen,34.4,18.4,184,3325,FEMALE 21 | Adelie,Torgersen,46,21.5,194,4200,MALE 22 | Adelie,Biscoe,37.8,18.3,174,3400,FEMALE 23 | Adelie,Biscoe,37.7,18.7,180,3600,MALE 24 | Adelie,Biscoe,35.9,19.2,189,3800,FEMALE 25 | Adelie,Biscoe,38.2,18.1,185,3950,MALE 26 | Adelie,Biscoe,38.8,17.2,180,3800,MALE 27 | Adelie,Biscoe,35.3,18.9,187,3800,FEMALE 28 | Adelie,Biscoe,40.6,18.6,183,3550,MALE 29 | Adelie,Biscoe,40.5,17.9,187,3200,FEMALE 30 | Adelie,Biscoe,37.9,18.6,172,3150,FEMALE 31 | Adelie,Biscoe,40.5,18.9,180,3950,MALE 32 | Adelie,Dream,39.5,16.7,178,3250,FEMALE 33 | Adelie,Dream,37.2,18.1,178,3900,MALE 34 | Adelie,Dream,39.5,17.8,188,3300,FEMALE 35 | Adelie,Dream,40.9,18.9,184,3900,MALE 36 | Adelie,Dream,36.4,17,195,3325,FEMALE 37 | Adelie,Dream,39.2,21.1,196,4150,MALE 38 | Adelie,Dream,38.8,20,190,3950,MALE 39 | Adelie,Dream,42.2,18.5,180,3550,FEMALE 40 | Adelie,Dream,37.6,19.3,181,3300,FEMALE 41 | Adelie,Dream,39.8,19.1,184,4650,MALE 42 | Adelie,Dream,36.5,18,182,3150,FEMALE 43 | Adelie,Dream,40.8,18.4,195,3900,MALE 44 | Adelie,Dream,36,18.5,186,3100,FEMALE 45 | Adelie,Dream,44.1,19.7,196,4400,MALE 46 | Adelie,Dream,37,16.9,185,3000,FEMALE 47 | Adelie,Dream,39.6,18.8,190,4600,MALE 48 | Adelie,Dream,41.1,19,182,3425,MALE 49 | Adelie,Dream,37.5,18.9,179,2975,NA 50 | Adelie,Dream,36,17.9,190,3450,FEMALE 51 | Adelie,Dream,42.3,21.2,191,4150,MALE 52 | Adelie,Biscoe,39.6,17.7,186,3500,FEMALE 53 | Adelie,Biscoe,40.1,18.9,188,4300,MALE 54 | Adelie,Biscoe,35,17.9,190,3450,FEMALE 55 | Adelie,Biscoe,42,19.5,200,4050,MALE 56 | Adelie,Biscoe,34.5,18.1,187,2900,FEMALE 57 | Adelie,Biscoe,41.4,18.6,191,3700,MALE 58 | Adelie,Biscoe,39,17.5,186,3550,FEMALE 59 | Adelie,Biscoe,40.6,18.8,193,3800,MALE 60 | Adelie,Biscoe,36.5,16.6,181,2850,FEMALE 61 | Adelie,Biscoe,37.6,19.1,194,3750,MALE 62 | Adelie,Biscoe,35.7,16.9,185,3150,FEMALE 63 | Adelie,Biscoe,41.3,21.1,195,4400,MALE 64 | Adelie,Biscoe,37.6,17,185,3600,FEMALE 65 | Adelie,Biscoe,41.1,18.2,192,4050,MALE 66 | Adelie,Biscoe,36.4,17.1,184,2850,FEMALE 67 | Adelie,Biscoe,41.6,18,192,3950,MALE 68 | Adelie,Biscoe,35.5,16.2,195,3350,FEMALE 69 | Adelie,Biscoe,41.1,19.1,188,4100,MALE 70 | Adelie,Torgersen,35.9,16.6,190,3050,FEMALE 71 | Adelie,Torgersen,41.8,19.4,198,4450,MALE 72 | Adelie,Torgersen,33.5,19,190,3600,FEMALE 73 | Adelie,Torgersen,39.7,18.4,190,3900,MALE 74 | Adelie,Torgersen,39.6,17.2,196,3550,FEMALE 75 | Adelie,Torgersen,45.8,18.9,197,4150,MALE 76 | Adelie,Torgersen,35.5,17.5,190,3700,FEMALE 77 | Adelie,Torgersen,42.8,18.5,195,4250,MALE 78 | Adelie,Torgersen,40.9,16.8,191,3700,FEMALE 79 | Adelie,Torgersen,37.2,19.4,184,3900,MALE 80 | Adelie,Torgersen,36.2,16.1,187,3550,FEMALE 81 | Adelie,Torgersen,42.1,19.1,195,4000,MALE 82 | Adelie,Torgersen,34.6,17.2,189,3200,FEMALE 83 | Adelie,Torgersen,42.9,17.6,196,4700,MALE 84 | Adelie,Torgersen,36.7,18.8,187,3800,FEMALE 85 | Adelie,Torgersen,35.1,19.4,193,4200,MALE 86 | Adelie,Dream,37.3,17.8,191,3350,FEMALE 87 | Adelie,Dream,41.3,20.3,194,3550,MALE 88 | Adelie,Dream,36.3,19.5,190,3800,MALE 89 | Adelie,Dream,36.9,18.6,189,3500,FEMALE 90 | Adelie,Dream,38.3,19.2,189,3950,MALE 91 | Adelie,Dream,38.9,18.8,190,3600,FEMALE 92 | Adelie,Dream,35.7,18,202,3550,FEMALE 93 | Adelie,Dream,41.1,18.1,205,4300,MALE 94 | Adelie,Dream,34,17.1,185,3400,FEMALE 95 | Adelie,Dream,39.6,18.1,186,4450,MALE 96 | Adelie,Dream,36.2,17.3,187,3300,FEMALE 97 | Adelie,Dream,40.8,18.9,208,4300,MALE 98 | Adelie,Dream,38.1,18.6,190,3700,FEMALE 99 | Adelie,Dream,40.3,18.5,196,4350,MALE 100 | Adelie,Dream,33.1,16.1,178,2900,FEMALE 101 | Adelie,Dream,43.2,18.5,192,4100,MALE 102 | Adelie,Biscoe,35,17.9,192,3725,FEMALE 103 | Adelie,Biscoe,41,20,203,4725,MALE 104 | Adelie,Biscoe,37.7,16,183,3075,FEMALE 105 | Adelie,Biscoe,37.8,20,190,4250,MALE 106 | Adelie,Biscoe,37.9,18.6,193,2925,FEMALE 107 | Adelie,Biscoe,39.7,18.9,184,3550,MALE 108 | Adelie,Biscoe,38.6,17.2,199,3750,FEMALE 109 | Adelie,Biscoe,38.2,20,190,3900,MALE 110 | Adelie,Biscoe,38.1,17,181,3175,FEMALE 111 | Adelie,Biscoe,43.2,19,197,4775,MALE 112 | Adelie,Biscoe,38.1,16.5,198,3825,FEMALE 113 | Adelie,Biscoe,45.6,20.3,191,4600,MALE 114 | Adelie,Biscoe,39.7,17.7,193,3200,FEMALE 115 | Adelie,Biscoe,42.2,19.5,197,4275,MALE 116 | Adelie,Biscoe,39.6,20.7,191,3900,FEMALE 117 | Adelie,Biscoe,42.7,18.3,196,4075,MALE 118 | Adelie,Torgersen,38.6,17,188,2900,FEMALE 119 | Adelie,Torgersen,37.3,20.5,199,3775,MALE 120 | Adelie,Torgersen,35.7,17,189,3350,FEMALE 121 | Adelie,Torgersen,41.1,18.6,189,3325,MALE 122 | Adelie,Torgersen,36.2,17.2,187,3150,FEMALE 123 | Adelie,Torgersen,37.7,19.8,198,3500,MALE 124 | Adelie,Torgersen,40.2,17,176,3450,FEMALE 125 | Adelie,Torgersen,41.4,18.5,202,3875,MALE 126 | Adelie,Torgersen,35.2,15.9,186,3050,FEMALE 127 | Adelie,Torgersen,40.6,19,199,4000,MALE 128 | Adelie,Torgersen,38.8,17.6,191,3275,FEMALE 129 | Adelie,Torgersen,41.5,18.3,195,4300,MALE 130 | Adelie,Torgersen,39,17.1,191,3050,FEMALE 131 | Adelie,Torgersen,44.1,18,210,4000,MALE 132 | Adelie,Torgersen,38.5,17.9,190,3325,FEMALE 133 | Adelie,Torgersen,43.1,19.2,197,3500,MALE 134 | Adelie,Dream,36.8,18.5,193,3500,FEMALE 135 | Adelie,Dream,37.5,18.5,199,4475,MALE 136 | Adelie,Dream,38.1,17.6,187,3425,FEMALE 137 | Adelie,Dream,41.1,17.5,190,3900,MALE 138 | Adelie,Dream,35.6,17.5,191,3175,FEMALE 139 | Adelie,Dream,40.2,20.1,200,3975,MALE 140 | Adelie,Dream,37,16.5,185,3400,FEMALE 141 | Adelie,Dream,39.7,17.9,193,4250,MALE 142 | Adelie,Dream,40.2,17.1,193,3400,FEMALE 143 | Adelie,Dream,40.6,17.2,187,3475,MALE 144 | Adelie,Dream,32.1,15.5,188,3050,FEMALE 145 | Adelie,Dream,40.7,17,190,3725,MALE 146 | Adelie,Dream,37.3,16.8,192,3000,FEMALE 147 | Adelie,Dream,39,18.7,185,3650,MALE 148 | Adelie,Dream,39.2,18.6,190,4250,MALE 149 | Adelie,Dream,36.6,18.4,184,3475,FEMALE 150 | Adelie,Dream,36,17.8,195,3450,FEMALE 151 | Adelie,Dream,37.8,18.1,193,3750,MALE 152 | Adelie,Dream,36,17.1,187,3700,FEMALE 153 | Adelie,Dream,41.5,18.5,201,4000,MALE 154 | Chinstrap,Dream,46.5,17.9,192,3500,FEMALE 155 | Chinstrap,Dream,50,19.5,196,3900,MALE 156 | Chinstrap,Dream,51.3,19.2,193,3650,MALE 157 | Chinstrap,Dream,45.4,18.7,188,3525,FEMALE 158 | Chinstrap,Dream,52.7,19.8,197,3725,MALE 159 | Chinstrap,Dream,45.2,17.8,198,3950,FEMALE 160 | Chinstrap,Dream,46.1,18.2,178,3250,FEMALE 161 | Chinstrap,Dream,51.3,18.2,197,3750,MALE 162 | Chinstrap,Dream,46,18.9,195,4150,FEMALE 163 | Chinstrap,Dream,51.3,19.9,198,3700,MALE 164 | Chinstrap,Dream,46.6,17.8,193,3800,FEMALE 165 | Chinstrap,Dream,51.7,20.3,194,3775,MALE 166 | Chinstrap,Dream,47,17.3,185,3700,FEMALE 167 | Chinstrap,Dream,52,18.1,201,4050,MALE 168 | Chinstrap,Dream,45.9,17.1,190,3575,FEMALE 169 | Chinstrap,Dream,50.5,19.6,201,4050,MALE 170 | Chinstrap,Dream,50.3,20,197,3300,MALE 171 | Chinstrap,Dream,58,17.8,181,3700,FEMALE 172 | Chinstrap,Dream,46.4,18.6,190,3450,FEMALE 173 | Chinstrap,Dream,49.2,18.2,195,4400,MALE 174 | Chinstrap,Dream,42.4,17.3,181,3600,FEMALE 175 | Chinstrap,Dream,48.5,17.5,191,3400,MALE 176 | Chinstrap,Dream,43.2,16.6,187,2900,FEMALE 177 | Chinstrap,Dream,50.6,19.4,193,3800,MALE 178 | Chinstrap,Dream,46.7,17.9,195,3300,FEMALE 179 | Chinstrap,Dream,52,19,197,4150,MALE 180 | Chinstrap,Dream,50.5,18.4,200,3400,FEMALE 181 | Chinstrap,Dream,49.5,19,200,3800,MALE 182 | Chinstrap,Dream,46.4,17.8,191,3700,FEMALE 183 | Chinstrap,Dream,52.8,20,205,4550,MALE 184 | Chinstrap,Dream,40.9,16.6,187,3200,FEMALE 185 | Chinstrap,Dream,54.2,20.8,201,4300,MALE 186 | Chinstrap,Dream,42.5,16.7,187,3350,FEMALE 187 | Chinstrap,Dream,51,18.8,203,4100,MALE 188 | Chinstrap,Dream,49.7,18.6,195,3600,MALE 189 | Chinstrap,Dream,47.5,16.8,199,3900,FEMALE 190 | Chinstrap,Dream,47.6,18.3,195,3850,FEMALE 191 | Chinstrap,Dream,52,20.7,210,4800,MALE 192 | Chinstrap,Dream,46.9,16.6,192,2700,FEMALE 193 | Chinstrap,Dream,53.5,19.9,205,4500,MALE 194 | Chinstrap,Dream,49,19.5,210,3950,MALE 195 | Chinstrap,Dream,46.2,17.5,187,3650,FEMALE 196 | Chinstrap,Dream,50.9,19.1,196,3550,MALE 197 | Chinstrap,Dream,45.5,17,196,3500,FEMALE 198 | Chinstrap,Dream,50.9,17.9,196,3675,FEMALE 199 | Chinstrap,Dream,50.8,18.5,201,4450,MALE 200 | Chinstrap,Dream,50.1,17.9,190,3400,FEMALE 201 | Chinstrap,Dream,49,19.6,212,4300,MALE 202 | Chinstrap,Dream,51.5,18.7,187,3250,MALE 203 | Chinstrap,Dream,49.8,17.3,198,3675,FEMALE 204 | Chinstrap,Dream,48.1,16.4,199,3325,FEMALE 205 | Chinstrap,Dream,51.4,19,201,3950,MALE 206 | Chinstrap,Dream,45.7,17.3,193,3600,FEMALE 207 | Chinstrap,Dream,50.7,19.7,203,4050,MALE 208 | Chinstrap,Dream,42.5,17.3,187,3350,FEMALE 209 | Chinstrap,Dream,52.2,18.8,197,3450,MALE 210 | Chinstrap,Dream,45.2,16.6,191,3250,FEMALE 211 | Chinstrap,Dream,49.3,19.9,203,4050,MALE 212 | Chinstrap,Dream,50.2,18.8,202,3800,MALE 213 | Chinstrap,Dream,45.6,19.4,194,3525,FEMALE 214 | Chinstrap,Dream,51.9,19.5,206,3950,MALE 215 | Chinstrap,Dream,46.8,16.5,189,3650,FEMALE 216 | Chinstrap,Dream,45.7,17,195,3650,FEMALE 217 | Chinstrap,Dream,55.8,19.8,207,4000,MALE 218 | Chinstrap,Dream,43.5,18.1,202,3400,FEMALE 219 | Chinstrap,Dream,49.6,18.2,193,3775,MALE 220 | Chinstrap,Dream,50.8,19,210,4100,MALE 221 | Chinstrap,Dream,50.2,18.7,198,3775,FEMALE 222 | Gentoo,Biscoe,46.1,13.2,211,4500,FEMALE 223 | Gentoo,Biscoe,50,16.3,230,5700,MALE 224 | Gentoo,Biscoe,48.7,14.1,210,4450,FEMALE 225 | Gentoo,Biscoe,50,15.2,218,5700,MALE 226 | Gentoo,Biscoe,47.6,14.5,215,5400,MALE 227 | Gentoo,Biscoe,46.5,13.5,210,4550,FEMALE 228 | Gentoo,Biscoe,45.4,14.6,211,4800,FEMALE 229 | Gentoo,Biscoe,46.7,15.3,219,5200,MALE 230 | Gentoo,Biscoe,43.3,13.4,209,4400,FEMALE 231 | Gentoo,Biscoe,46.8,15.4,215,5150,MALE 232 | Gentoo,Biscoe,40.9,13.7,214,4650,FEMALE 233 | Gentoo,Biscoe,49,16.1,216,5550,MALE 234 | Gentoo,Biscoe,45.5,13.7,214,4650,FEMALE 235 | Gentoo,Biscoe,48.4,14.6,213,5850,MALE 236 | Gentoo,Biscoe,45.8,14.6,210,4200,FEMALE 237 | Gentoo,Biscoe,49.3,15.7,217,5850,MALE 238 | Gentoo,Biscoe,42,13.5,210,4150,FEMALE 239 | Gentoo,Biscoe,49.2,15.2,221,6300,MALE 240 | Gentoo,Biscoe,46.2,14.5,209,4800,FEMALE 241 | Gentoo,Biscoe,48.7,15.1,222,5350,MALE 242 | Gentoo,Biscoe,50.2,14.3,218,5700,MALE 243 | Gentoo,Biscoe,45.1,14.5,215,5000,FEMALE 244 | Gentoo,Biscoe,46.5,14.5,213,4400,FEMALE 245 | Gentoo,Biscoe,46.3,15.8,215,5050,MALE 246 | Gentoo,Biscoe,42.9,13.1,215,5000,FEMALE 247 | Gentoo,Biscoe,46.1,15.1,215,5100,MALE 248 | Gentoo,Biscoe,44.5,14.3,216,4100,NA 249 | Gentoo,Biscoe,47.8,15,215,5650,MALE 250 | Gentoo,Biscoe,48.2,14.3,210,4600,FEMALE 251 | Gentoo,Biscoe,50,15.3,220,5550,MALE 252 | Gentoo,Biscoe,47.3,15.3,222,5250,MALE 253 | Gentoo,Biscoe,42.8,14.2,209,4700,FEMALE 254 | Gentoo,Biscoe,45.1,14.5,207,5050,FEMALE 255 | Gentoo,Biscoe,59.6,17,230,6050,MALE 256 | Gentoo,Biscoe,49.1,14.8,220,5150,FEMALE 257 | Gentoo,Biscoe,48.4,16.3,220,5400,MALE 258 | Gentoo,Biscoe,42.6,13.7,213,4950,FEMALE 259 | Gentoo,Biscoe,44.4,17.3,219,5250,MALE 260 | Gentoo,Biscoe,44,13.6,208,4350,FEMALE 261 | Gentoo,Biscoe,48.7,15.7,208,5350,MALE 262 | Gentoo,Biscoe,42.7,13.7,208,3950,FEMALE 263 | Gentoo,Biscoe,49.6,16,225,5700,MALE 264 | Gentoo,Biscoe,45.3,13.7,210,4300,FEMALE 265 | Gentoo,Biscoe,49.6,15,216,4750,MALE 266 | Gentoo,Biscoe,50.5,15.9,222,5550,MALE 267 | Gentoo,Biscoe,43.6,13.9,217,4900,FEMALE 268 | Gentoo,Biscoe,45.5,13.9,210,4200,FEMALE 269 | Gentoo,Biscoe,50.5,15.9,225,5400,MALE 270 | Gentoo,Biscoe,44.9,13.3,213,5100,FEMALE 271 | Gentoo,Biscoe,45.2,15.8,215,5300,MALE 272 | Gentoo,Biscoe,46.6,14.2,210,4850,FEMALE 273 | Gentoo,Biscoe,48.5,14.1,220,5300,MALE 274 | Gentoo,Biscoe,45.1,14.4,210,4400,FEMALE 275 | Gentoo,Biscoe,50.1,15,225,5000,MALE 276 | Gentoo,Biscoe,46.5,14.4,217,4900,FEMALE 277 | Gentoo,Biscoe,45,15.4,220,5050,MALE 278 | Gentoo,Biscoe,43.8,13.9,208,4300,FEMALE 279 | Gentoo,Biscoe,45.5,15,220,5000,MALE 280 | Gentoo,Biscoe,43.2,14.5,208,4450,FEMALE 281 | Gentoo,Biscoe,50.4,15.3,224,5550,MALE 282 | Gentoo,Biscoe,45.3,13.8,208,4200,FEMALE 283 | Gentoo,Biscoe,46.2,14.9,221,5300,MALE 284 | Gentoo,Biscoe,45.7,13.9,214,4400,FEMALE 285 | Gentoo,Biscoe,54.3,15.7,231,5650,MALE 286 | Gentoo,Biscoe,45.8,14.2,219,4700,FEMALE 287 | Gentoo,Biscoe,49.8,16.8,230,5700,MALE 288 | Gentoo,Biscoe,46.2,14.4,214,4650,NA 289 | Gentoo,Biscoe,49.5,16.2,229,5800,MALE 290 | Gentoo,Biscoe,43.5,14.2,220,4700,FEMALE 291 | Gentoo,Biscoe,50.7,15,223,5550,MALE 292 | Gentoo,Biscoe,47.7,15,216,4750,FEMALE 293 | Gentoo,Biscoe,46.4,15.6,221,5000,MALE 294 | Gentoo,Biscoe,48.2,15.6,221,5100,MALE 295 | Gentoo,Biscoe,46.5,14.8,217,5200,FEMALE 296 | Gentoo,Biscoe,46.4,15,216,4700,FEMALE 297 | Gentoo,Biscoe,48.6,16,230,5800,MALE 298 | Gentoo,Biscoe,47.5,14.2,209,4600,FEMALE 299 | Gentoo,Biscoe,51.1,16.3,220,6000,MALE 300 | Gentoo,Biscoe,45.2,13.8,215,4750,FEMALE 301 | Gentoo,Biscoe,45.2,16.4,223,5950,MALE 302 | Gentoo,Biscoe,49.1,14.5,212,4625,FEMALE 303 | Gentoo,Biscoe,52.5,15.6,221,5450,MALE 304 | Gentoo,Biscoe,47.4,14.6,212,4725,FEMALE 305 | Gentoo,Biscoe,50,15.9,224,5350,MALE 306 | Gentoo,Biscoe,44.9,13.8,212,4750,FEMALE 307 | Gentoo,Biscoe,50.8,17.3,228,5600,MALE 308 | Gentoo,Biscoe,43.4,14.4,218,4600,FEMALE 309 | Gentoo,Biscoe,51.3,14.2,218,5300,MALE 310 | Gentoo,Biscoe,47.5,14,212,4875,FEMALE 311 | Gentoo,Biscoe,52.1,17,230,5550,MALE 312 | Gentoo,Biscoe,47.5,15,218,4950,FEMALE 313 | Gentoo,Biscoe,52.2,17.1,228,5400,MALE 314 | Gentoo,Biscoe,45.5,14.5,212,4750,FEMALE 315 | Gentoo,Biscoe,49.5,16.1,224,5650,MALE 316 | Gentoo,Biscoe,44.5,14.7,214,4850,FEMALE 317 | Gentoo,Biscoe,50.8,15.7,226,5200,MALE 318 | Gentoo,Biscoe,49.4,15.8,216,4925,MALE 319 | Gentoo,Biscoe,46.9,14.6,222,4875,FEMALE 320 | Gentoo,Biscoe,48.4,14.4,203,4625,FEMALE 321 | Gentoo,Biscoe,51.1,16.5,225,5250,MALE 322 | Gentoo,Biscoe,48.5,15,219,4850,FEMALE 323 | Gentoo,Biscoe,55.9,17,228,5600,MALE 324 | Gentoo,Biscoe,47.2,15.5,215,4975,FEMALE 325 | Gentoo,Biscoe,49.1,15,228,5500,MALE 326 | Gentoo,Biscoe,47.3,13.8,216,4725,NA 327 | Gentoo,Biscoe,46.8,16.1,215,5500,MALE 328 | Gentoo,Biscoe,41.7,14.7,210,4700,FEMALE 329 | Gentoo,Biscoe,53.4,15.8,219,5500,MALE 330 | Gentoo,Biscoe,43.3,14,208,4575,FEMALE 331 | Gentoo,Biscoe,48.1,15.1,209,5500,MALE 332 | Gentoo,Biscoe,50.5,15.2,216,5000,FEMALE 333 | Gentoo,Biscoe,49.8,15.9,229,5950,MALE 334 | Gentoo,Biscoe,43.5,15.2,213,4650,FEMALE 335 | Gentoo,Biscoe,51.5,16.3,230,5500,MALE 336 | Gentoo,Biscoe,46.2,14.1,217,4375,FEMALE 337 | Gentoo,Biscoe,55.1,16,230,5850,MALE 338 | Gentoo,Biscoe,44.5,15.7,217,4875,. 339 | Gentoo,Biscoe,48.8,16.2,222,6000,MALE 340 | Gentoo,Biscoe,47.2,13.7,214,4925,FEMALE 341 | Gentoo,Biscoe,NA,NA,NA,NA,NA 342 | Gentoo,Biscoe,46.8,14.3,215,4850,FEMALE 343 | Gentoo,Biscoe,50.4,15.7,222,5750,MALE 344 | Gentoo,Biscoe,45.2,14.8,212,5200,FEMALE 345 | Gentoo,Biscoe,49.9,16.1,213,5400,MALE 346 | -------------------------------------------------------------------------------- /MySQL-with-Ollama/customer.csv: -------------------------------------------------------------------------------- 1 | Customer ID,Gender,Age,City,Membership Type,Total Spend,Items Purchased,Average Rating,Discount Applied,Days Since Last Purchase,Satisfaction Level 2 | 101,Female,29,New York,Gold,1120.2,14,4.6,TRUE,25,Satisfied 3 | 102,Male,34,Los Angeles,Silver,780.5,11,4.1,FALSE,18,Neutral 4 | 103,Female,43,Chicago,Bronze,510.75,9,3.4,TRUE,42,Unsatisfied 5 | 104,Male,30,San Francisco,Gold,1480.3,19,4.7,FALSE,12,Satisfied 6 | 105,Male,27,Miami,Silver,720.4,13,4,TRUE,55,Unsatisfied 7 | 106,Female,37,Houston,Bronze,440.8,8,3.1,FALSE,22,Neutral 8 | 107,Female,31,New York,Gold,1150.6,15,4.5,TRUE,28,Satisfied 9 | 108,Male,35,Los Angeles,Silver,800.9,12,4.2,FALSE,14,Neutral 10 | 109,Female,41,Chicago,Bronze,495.25,10,3.6,TRUE,40,Unsatisfied 11 | 110,Male,28,San Francisco,Gold,1520.1,21,4.8,FALSE,9,Satisfied 12 | 111,Male,32,Miami,Silver,690.3,11,3.8,TRUE,34,Unsatisfied 13 | 112,Female,36,Houston,Bronze,470.5,7,3.2,FALSE,20,Neutral 14 | 113,Female,30,New York,Gold,1200.8,16,4.3,TRUE,21,Satisfied 15 | 114,Male,33,Los Angeles,Silver,820.75,13,4.4,FALSE,15,Satisfied 16 | 115,Female,42,Chicago,Bronze,530.4,9,3.5,TRUE,38,Unsatisfied 17 | 116,Male,29,San Francisco,Gold,1360.2,18,4.9,FALSE,11,Satisfied 18 | 117,Male,26,Miami,Silver,700.6,12,3.7,TRUE,48,Unsatisfied 19 | 118,Female,38,Houston,Bronze,450.9,8,3,FALSE,25,Neutral 20 | 119,Female,32,New York,Gold,1170.3,14,4.7,TRUE,29,Satisfied 21 | 120,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,16,Neutral 22 | 121,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,41,Unsatisfied 23 | 122,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 24 | 123,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 25 | 124,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 26 | 125,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 27 | 126,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 28 | 127,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 29 | 128,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 30 | 129,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 31 | 130,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 32 | 131,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 33 | 132,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 34 | 133,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 35 | 134,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 36 | 135,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 37 | 136,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 38 | 137,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 39 | 138,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 40 | 139,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 41 | 140,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 42 | 141,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 43 | 142,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21,Neutral 44 | 143,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 45 | 144,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 46 | 145,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 47 | 146,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 48 | 147,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 49 | 148,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 50 | 149,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 51 | 150,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 52 | 151,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 53 | 152,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 54 | 153,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 55 | 154,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 56 | 155,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 57 | 156,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 58 | 157,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 59 | 158,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 60 | 159,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 61 | 160,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 62 | 161,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 63 | 162,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 64 | 163,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 65 | 164,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 66 | 165,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 67 | 166,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 68 | 167,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 69 | 168,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 70 | 169,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 71 | 170,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 72 | 171,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 73 | 172,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21, 74 | 173,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 75 | 174,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 76 | 175,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 77 | 176,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 78 | 177,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 79 | 178,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 80 | 179,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 81 | 180,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 82 | 181,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 83 | 182,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 84 | 183,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 85 | 184,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 86 | 185,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 87 | 186,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 88 | 187,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 89 | 188,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 90 | 189,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 91 | 190,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 92 | 191,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 93 | 192,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 94 | 193,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 95 | 194,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 96 | 195,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 97 | 196,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 98 | 197,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 99 | 198,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 100 | 199,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 101 | 200,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 102 | 201,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 103 | 202,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21,Neutral 104 | 203,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 105 | 204,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 106 | 205,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 107 | 206,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 108 | 207,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 109 | 208,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 110 | 209,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 111 | 210,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 112 | 211,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 113 | 212,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 114 | 213,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 115 | 214,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 116 | 215,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 117 | 216,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 118 | 217,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 119 | 218,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 120 | 219,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 121 | 220,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 122 | 221,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 123 | 222,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 124 | 223,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 125 | 224,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 126 | 225,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 127 | 226,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 128 | 227,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 129 | 228,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 130 | 229,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 131 | 230,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 132 | 231,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 133 | 232,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21,Neutral 134 | 233,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 135 | 234,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 136 | 235,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 137 | 236,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 138 | 237,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 139 | 238,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 140 | 239,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 141 | 240,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 142 | 241,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 143 | 242,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 144 | 243,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 145 | 244,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23, 146 | 245,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 147 | 246,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 148 | 247,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 149 | 248,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 150 | 249,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 151 | 250,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 152 | 251,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 153 | 252,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 154 | 253,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 155 | 254,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 156 | 255,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 157 | 256,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 158 | 257,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 159 | 258,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 160 | 259,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 161 | 260,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 162 | 261,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 163 | 262,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 164 | 263,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 165 | 264,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 166 | 265,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 167 | 266,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 168 | 267,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 169 | 268,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 170 | 269,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 171 | 270,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 172 | 271,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 173 | 272,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 174 | 273,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 175 | 274,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 176 | 275,Female,31,New York,Gold,1140.6,15,4.5,TRUE,27,Satisfied 177 | 276,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 178 | 277,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 179 | 278,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 180 | 279,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 181 | 280,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 182 | 281,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 183 | 282,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 184 | 283,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 185 | 284,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 186 | 285,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 187 | 286,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 188 | 287,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 189 | 288,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,13,Neutral 190 | 289,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 191 | 290,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 192 | 291,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 193 | 292,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 194 | 293,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 195 | 294,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 196 | 295,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 197 | 296,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 198 | 297,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 199 | 298,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 200 | 299,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 201 | 300,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 202 | 301,Male,36,San Francisco,Gold,1420.8,19,4.6,FALSE,11,Satisfied 203 | 302,Female,29,Miami,Silver,730.4,14,4,TRUE,50,Unsatisfied 204 | 303,Female,35,Houston,Bronze,450.6,7,3.3,FALSE,21,Neutral 205 | 304,Male,31,New York,Gold,1210.6,17,4.8,TRUE,18,Satisfied 206 | 305,Male,27,Los Angeles,Silver,780.9,11,4.2,FALSE,16,Neutral 207 | 306,Female,42,Chicago,Bronze,495.25,10,3.5,TRUE,35,Unsatisfied 208 | 307,Male,29,San Francisco,Gold,1390.2,18,4.7,FALSE,9,Satisfied 209 | 308,Male,26,Miami,Silver,670.6,12,3.9,TRUE,46,Unsatisfied 210 | 309,Female,38,Houston,Bronze,460.9,8,3.2,FALSE,25,Neutral 211 | 310,Female,32,New York,Gold,1170.3,14,4.5,TRUE,21,Satisfied 212 | 311,Male,34,Los Angeles,Silver,810.2,11,4,FALSE,14,Neutral 213 | 312,Female,43,Chicago,Bronze,515.75,9,3.3,TRUE,38,Unsatisfied 214 | 313,Male,30,San Francisco,Gold,1450.5,20,4.8,FALSE,12,Satisfied 215 | 314,Male,27,Miami,Silver,690.4,13,4.1,TRUE,52,Unsatisfied 216 | 315,Female,37,Houston,Bronze,420.8,7,3.4,FALSE,24,Neutral 217 | 316,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 218 | 317,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,13,Neutral 219 | 318,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,39,Unsatisfied 220 | 319,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 221 | 320,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 222 | 321,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 223 | 322,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 224 | 323,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 225 | 324,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 226 | 325,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 227 | 326,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 228 | 327,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 229 | 328,Female,31,New York,Gold,1140.6,15,4.5,TRUE,27,Satisfied 230 | 329,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,13,Neutral 231 | 330,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 232 | 331,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 233 | 332,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 234 | 333,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 235 | 334,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 236 | 335,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 237 | 336,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 238 | 337,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 239 | 338,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 240 | 339,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 241 | 340,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 242 | 341,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 243 | 342,Female,43,Chicago,Bronze,525.75,9,3.3,TRUE,40,Unsatisfied 244 | 343,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,11,Satisfied 245 | 344,Male,27,Miami,Silver,720.4,13,4,TRUE,55,Unsatisfied 246 | 345,Female,37,Houston,Bronze,440.8,7,3.2,FALSE,25,Neutral 247 | 346,Female,31,New York,Gold,1150.6,15,4.5,TRUE,28,Satisfied 248 | 347,Male,35,Los Angeles,Silver,830.9,12,4.3,FALSE,14,Neutral 249 | 348,Female,41,Chicago,Bronze,490.25,9,3.6,TRUE,40,Unsatisfied 250 | 349,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,10,Satisfied 251 | 350,Male,32,Miami,Silver,690.3,10,3.8,TRUE,34,Unsatisfied 252 | 351,Female,36,Houston,Bronze,480.5,8,3.1,FALSE,20,Neutral 253 | 352,Female,30,New York,Gold,1170.8,16,4.7,TRUE,21,Satisfied 254 | 353,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 255 | 354,Female,43,Chicago,Bronze,510.75,10,3.3,TRUE,41,Unsatisfied 256 | 355,Male,30,San Francisco,Gold,1440.5,19,4.6,FALSE,12,Satisfied 257 | 356,Male,27,Miami,Silver,700.4,13,4.1,TRUE,56,Unsatisfied 258 | 357,Female,37,Houston,Bronze,410.8,7,3.4,FALSE,26,Neutral 259 | 358,Female,31,New York,Gold,1160.6,15,4.5,TRUE,29,Satisfied 260 | 359,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,14,Neutral 261 | 360,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,41,Unsatisfied 262 | 361,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,10,Satisfied 263 | 362,Male,32,Miami,Silver,660.3,10,3.8,TRUE,35,Unsatisfied 264 | 363,Female,36,Houston,Bronze,430.5,8,3.1,FALSE,21,Neutral 265 | 364,Female,30,New York,Gold,1200.8,16,4.7,TRUE,22,Satisfied 266 | 365,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,16,Neutral 267 | 366,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,42,Unsatisfied 268 | 367,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,13,Satisfied 269 | 368,Male,27,Miami,Silver,710.4,13,4.1,TRUE,57,Unsatisfied 270 | 369,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,24,Neutral 271 | 370,Female,31,New York,Gold,1140.6,15,4.5,TRUE,30,Satisfied 272 | 371,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,17,Neutral 273 | 372,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,42,Unsatisfied 274 | 373,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,11,Satisfied 275 | 374,Male,32,Miami,Silver,660.3,10,3.8,TRUE,36,Unsatisfied 276 | 375,Female,36,Houston,Bronze,470.5,8,3,FALSE,22,Neutral 277 | 376,Female,30,New York,Gold,1190.8,16,4.5,TRUE,23,Satisfied 278 | 377,Male,33,Los Angeles,Silver,770.2,11,4.2,FALSE,18,Neutral 279 | 378,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,43,Unsatisfied 280 | 379,Male,30,San Francisco,Gold,1450.5,19,4.6,FALSE,12,Satisfied 281 | 380,Male,27,Miami,Silver,710.4,13,4.1,TRUE,58,Unsatisfied 282 | 381,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,27,Neutral 283 | 382,Female,31,New York,Gold,1160.6,15,4.5,TRUE,31,Satisfied 284 | 383,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,15,Neutral 285 | 384,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,43,Unsatisfied 286 | 385,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,10,Satisfied 287 | 386,Male,32,Miami,Silver,660.3,10,3.8,TRUE,37,Unsatisfied 288 | 387,Female,36,Houston,Bronze,440.5,8,3.1,FALSE,23,Neutral 289 | 388,Female,30,New York,Gold,1200.8,16,4.7,TRUE,24,Satisfied 290 | 389,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,16,Neutral 291 | 390,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,44,Unsatisfied 292 | 391,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,13,Satisfied 293 | 392,Male,27,Miami,Silver,710.4,13,4.1,TRUE,59,Unsatisfied 294 | 393,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,28,Neutral 295 | 394,Female,31,New York,Gold,1140.6,15,4.5,TRUE,32,Satisfied 296 | 395,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,17,Neutral 297 | 396,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,44,Unsatisfied 298 | 397,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,12,Satisfied 299 | 398,Male,32,Miami,Silver,660.3,10,3.8,TRUE,38,Unsatisfied 300 | 399,Female,36,Houston,Bronze,470.5,8,3,FALSE,22,Neutral 301 | 400,Female,30,New York,Gold,1190.8,16,4.5,TRUE,25,Satisfied 302 | 401,Male,33,Los Angeles,Silver,800.2,11,4.1,FALSE,18,Neutral 303 | 402,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,45,Unsatisfied 304 | 403,Male,30,San Francisco,Gold,1450.5,19,4.6,FALSE,13,Satisfied 305 | 404,Male,27,Miami,Silver,710.4,13,4.1,TRUE,60,Unsatisfied 306 | 405,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,29,Neutral 307 | 406,Female,31,New York,Gold,1160.6,15,4.5,TRUE,33,Satisfied 308 | 407,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,16,Neutral 309 | 408,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,45,Unsatisfied 310 | 409,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,11,Satisfied 311 | 410,Male,32,Miami,Silver,660.3,10,3.8,TRUE,39,Unsatisfied 312 | 411,Female,36,Houston,Bronze,440.5,8,3.1,FALSE,24,Neutral 313 | 412,Female,30,New York,Gold,1200.8,16,4.7,TRUE,25,Satisfied 314 | 413,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,17,Neutral 315 | 414,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,46,Unsatisfied 316 | 415,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,14,Satisfied 317 | 416,Male,27,Miami,Silver,710.4,13,4.1,TRUE,61,Unsatisfied 318 | 417,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,30,Neutral 319 | 418,Female,31,New York,Gold,1140.6,15,4.5,TRUE,34,Satisfied 320 | 419,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,19,Neutral 321 | 420,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,46,Unsatisfied 322 | 421,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,12,Satisfied 323 | 422,Male,32,Miami,Silver,660.3,10,3.8,TRUE,40,Unsatisfied 324 | 423,Female,36,Houston,Bronze,470.5,8,3,FALSE,26,Neutral 325 | 424,Female,30,New York,Gold,1190.8,16,4.5,TRUE,27,Satisfied 326 | 425,Male,34,Los Angeles,Silver,780.2,11,4.2,FALSE,18,Neutral 327 | 426,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,47,Unsatisfied 328 | 427,Male,30,San Francisco,Gold,1450.5,19,4.6,FALSE,14,Satisfied 329 | 428,Male,27,Miami,Silver,710.4,13,4.1,TRUE,62,Unsatisfied 330 | 429,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,31,Neutral 331 | 430,Female,31,New York,Gold,1160.6,15,4.5,TRUE,35,Satisfied 332 | 431,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,20,Neutral 333 | 432,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,47,Unsatisfied 334 | 433,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,11,Satisfied 335 | 434,Male,32,Miami,Silver,660.3,10,3.8,TRUE,41,Unsatisfied 336 | 435,Female,36,Houston,Bronze,440.5,8,3.1,FALSE,28,Neutral 337 | 436,Female,30,New York,Gold,1200.8,16,4.7,TRUE,28,Satisfied 338 | 437,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,19,Neutral 339 | 438,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,48,Unsatisfied 340 | 439,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,15,Satisfied 341 | 440,Male,27,Miami,Silver,710.4,13,4.1,TRUE,63,Unsatisfied 342 | 441,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,32,Neutral 343 | 442,Female,31,New York,Gold,1140.6,15,4.5,TRUE,36,Satisfied 344 | 443,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,20,Neutral 345 | 444,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,48,Unsatisfied 346 | 445,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,13,Satisfied 347 | 446,Male,32,Miami,Silver,660.3,10,3.8,TRUE,42,Unsatisfied 348 | 447,Female,36,Houston,Bronze,470.5,8,3,FALSE,27,Neutral 349 | 448,Female,30,New York,Gold,1190.8,16,4.5,TRUE,28,Satisfied 350 | 449,Male,34,Los Angeles,Silver,780.2,11,4.2,FALSE,21,Neutral 351 | 450,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,49,Unsatisfied -------------------------------------------------------------------------------- /Datasets/E-commerce-Customer-Behavior.csv: -------------------------------------------------------------------------------- 1 | Customer ID,Gender,Age,City,Membership Type,Total Spend,Items Purchased,Average Rating,Discount Applied,Days Since Last Purchase,Satisfaction Level 2 | 101,Female,29,New York,Gold,1120.2,14,4.6,TRUE,25,Satisfied 3 | 102,Male,34,Los Angeles,Silver,780.5,11,4.1,FALSE,18,Neutral 4 | 103,Female,43,Chicago,Bronze,510.75,9,3.4,TRUE,42,Unsatisfied 5 | 104,Male,30,San Francisco,Gold,1480.3,19,4.7,FALSE,12,Satisfied 6 | 105,Male,27,Miami,Silver,720.4,13,4,TRUE,55,Unsatisfied 7 | 106,Female,37,Houston,Bronze,440.8,8,3.1,FALSE,22,Neutral 8 | 107,Female,31,New York,Gold,1150.6,15,4.5,TRUE,28,Satisfied 9 | 108,Male,35,Los Angeles,Silver,800.9,12,4.2,FALSE,14,Neutral 10 | 109,Female,41,Chicago,Bronze,495.25,10,3.6,TRUE,40,Unsatisfied 11 | 110,Male,28,San Francisco,Gold,1520.1,21,4.8,FALSE,9,Satisfied 12 | 111,Male,32,Miami,Silver,690.3,11,3.8,TRUE,34,Unsatisfied 13 | 112,Female,36,Houston,Bronze,470.5,7,3.2,FALSE,20,Neutral 14 | 113,Female,30,New York,Gold,1200.8,16,4.3,TRUE,21,Satisfied 15 | 114,Male,33,Los Angeles,Silver,820.75,13,4.4,FALSE,15,Satisfied 16 | 115,Female,42,Chicago,Bronze,530.4,9,3.5,TRUE,38,Unsatisfied 17 | 116,Male,29,San Francisco,Gold,1360.2,18,4.9,FALSE,11,Satisfied 18 | 117,Male,26,Miami,Silver,700.6,12,3.7,TRUE,48,Unsatisfied 19 | 118,Female,38,Houston,Bronze,450.9,8,3,FALSE,25,Neutral 20 | 119,Female,32,New York,Gold,1170.3,14,4.7,TRUE,29,Satisfied 21 | 120,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,16,Neutral 22 | 121,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,41,Unsatisfied 23 | 122,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 24 | 123,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 25 | 124,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 26 | 125,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 27 | 126,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 28 | 127,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 29 | 128,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 30 | 129,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 31 | 130,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 32 | 131,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 33 | 132,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 34 | 133,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 35 | 134,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 36 | 135,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 37 | 136,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 38 | 137,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 39 | 138,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 40 | 139,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 41 | 140,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 42 | 141,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 43 | 142,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21,Neutral 44 | 143,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 45 | 144,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 46 | 145,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 47 | 146,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 48 | 147,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 49 | 148,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 50 | 149,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 51 | 150,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 52 | 151,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 53 | 152,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 54 | 153,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 55 | 154,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 56 | 155,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 57 | 156,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 58 | 157,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 59 | 158,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 60 | 159,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 61 | 160,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 62 | 161,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 63 | 162,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 64 | 163,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 65 | 164,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 66 | 165,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 67 | 166,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 68 | 167,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 69 | 168,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 70 | 169,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 71 | 170,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 72 | 171,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 73 | 172,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21, 74 | 173,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 75 | 174,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 76 | 175,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 77 | 176,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 78 | 177,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 79 | 178,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 80 | 179,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 81 | 180,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 82 | 181,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 83 | 182,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 84 | 183,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 85 | 184,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 86 | 185,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 87 | 186,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 88 | 187,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 89 | 188,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 90 | 189,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 91 | 190,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 92 | 191,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 93 | 192,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 94 | 193,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 95 | 194,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 96 | 195,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 97 | 196,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 98 | 197,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 99 | 198,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 100 | 199,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 101 | 200,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 102 | 201,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 103 | 202,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21,Neutral 104 | 203,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 105 | 204,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 106 | 205,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 107 | 206,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 108 | 207,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 109 | 208,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 110 | 209,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 111 | 210,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 112 | 211,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 113 | 212,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 114 | 213,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 115 | 214,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 116 | 215,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 117 | 216,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 118 | 217,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 119 | 218,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 120 | 219,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 121 | 220,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 122 | 221,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 123 | 222,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 124 | 223,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 125 | 224,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 126 | 225,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 127 | 226,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 128 | 227,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 129 | 228,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 130 | 229,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 131 | 230,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 132 | 231,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 133 | 232,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21,Neutral 134 | 233,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 135 | 234,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 136 | 235,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 137 | 236,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 138 | 237,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 139 | 238,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 140 | 239,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 141 | 240,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 142 | 241,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 143 | 242,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 144 | 243,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 145 | 244,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23, 146 | 245,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 147 | 246,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 148 | 247,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 149 | 248,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 150 | 249,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 151 | 250,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 152 | 251,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 153 | 252,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 154 | 253,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 155 | 254,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 156 | 255,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 157 | 256,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 158 | 257,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 159 | 258,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 160 | 259,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 161 | 260,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 162 | 261,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 163 | 262,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 164 | 263,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 165 | 264,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 166 | 265,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 167 | 266,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 168 | 267,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 169 | 268,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 170 | 269,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 171 | 270,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 172 | 271,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 173 | 272,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 174 | 273,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 175 | 274,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 176 | 275,Female,31,New York,Gold,1140.6,15,4.5,TRUE,27,Satisfied 177 | 276,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 178 | 277,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 179 | 278,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 180 | 279,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 181 | 280,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 182 | 281,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 183 | 282,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 184 | 283,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 185 | 284,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 186 | 285,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 187 | 286,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 188 | 287,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 189 | 288,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,13,Neutral 190 | 289,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 191 | 290,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 192 | 291,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 193 | 292,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 194 | 293,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 195 | 294,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 196 | 295,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 197 | 296,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 198 | 297,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 199 | 298,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 200 | 299,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 201 | 300,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 202 | 301,Male,36,San Francisco,Gold,1420.8,19,4.6,FALSE,11,Satisfied 203 | 302,Female,29,Miami,Silver,730.4,14,4,TRUE,50,Unsatisfied 204 | 303,Female,35,Houston,Bronze,450.6,7,3.3,FALSE,21,Neutral 205 | 304,Male,31,New York,Gold,1210.6,17,4.8,TRUE,18,Satisfied 206 | 305,Male,27,Los Angeles,Silver,780.9,11,4.2,FALSE,16,Neutral 207 | 306,Female,42,Chicago,Bronze,495.25,10,3.5,TRUE,35,Unsatisfied 208 | 307,Male,29,San Francisco,Gold,1390.2,18,4.7,FALSE,9,Satisfied 209 | 308,Male,26,Miami,Silver,670.6,12,3.9,TRUE,46,Unsatisfied 210 | 309,Female,38,Houston,Bronze,460.9,8,3.2,FALSE,25,Neutral 211 | 310,Female,32,New York,Gold,1170.3,14,4.5,TRUE,21,Satisfied 212 | 311,Male,34,Los Angeles,Silver,810.2,11,4,FALSE,14,Neutral 213 | 312,Female,43,Chicago,Bronze,515.75,9,3.3,TRUE,38,Unsatisfied 214 | 313,Male,30,San Francisco,Gold,1450.5,20,4.8,FALSE,12,Satisfied 215 | 314,Male,27,Miami,Silver,690.4,13,4.1,TRUE,52,Unsatisfied 216 | 315,Female,37,Houston,Bronze,420.8,7,3.4,FALSE,24,Neutral 217 | 316,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 218 | 317,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,13,Neutral 219 | 318,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,39,Unsatisfied 220 | 319,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 221 | 320,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 222 | 321,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 223 | 322,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 224 | 323,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 225 | 324,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 226 | 325,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 227 | 326,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 228 | 327,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 229 | 328,Female,31,New York,Gold,1140.6,15,4.5,TRUE,27,Satisfied 230 | 329,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,13,Neutral 231 | 330,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 232 | 331,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 233 | 332,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 234 | 333,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 235 | 334,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 236 | 335,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 237 | 336,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 238 | 337,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 239 | 338,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 240 | 339,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 241 | 340,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 242 | 341,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 243 | 342,Female,43,Chicago,Bronze,525.75,9,3.3,TRUE,40,Unsatisfied 244 | 343,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,11,Satisfied 245 | 344,Male,27,Miami,Silver,720.4,13,4,TRUE,55,Unsatisfied 246 | 345,Female,37,Houston,Bronze,440.8,7,3.2,FALSE,25,Neutral 247 | 346,Female,31,New York,Gold,1150.6,15,4.5,TRUE,28,Satisfied 248 | 347,Male,35,Los Angeles,Silver,830.9,12,4.3,FALSE,14,Neutral 249 | 348,Female,41,Chicago,Bronze,490.25,9,3.6,TRUE,40,Unsatisfied 250 | 349,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,10,Satisfied 251 | 350,Male,32,Miami,Silver,690.3,10,3.8,TRUE,34,Unsatisfied 252 | 351,Female,36,Houston,Bronze,480.5,8,3.1,FALSE,20,Neutral 253 | 352,Female,30,New York,Gold,1170.8,16,4.7,TRUE,21,Satisfied 254 | 353,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 255 | 354,Female,43,Chicago,Bronze,510.75,10,3.3,TRUE,41,Unsatisfied 256 | 355,Male,30,San Francisco,Gold,1440.5,19,4.6,FALSE,12,Satisfied 257 | 356,Male,27,Miami,Silver,700.4,13,4.1,TRUE,56,Unsatisfied 258 | 357,Female,37,Houston,Bronze,410.8,7,3.4,FALSE,26,Neutral 259 | 358,Female,31,New York,Gold,1160.6,15,4.5,TRUE,29,Satisfied 260 | 359,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,14,Neutral 261 | 360,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,41,Unsatisfied 262 | 361,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,10,Satisfied 263 | 362,Male,32,Miami,Silver,660.3,10,3.8,TRUE,35,Unsatisfied 264 | 363,Female,36,Houston,Bronze,430.5,8,3.1,FALSE,21,Neutral 265 | 364,Female,30,New York,Gold,1200.8,16,4.7,TRUE,22,Satisfied 266 | 365,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,16,Neutral 267 | 366,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,42,Unsatisfied 268 | 367,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,13,Satisfied 269 | 368,Male,27,Miami,Silver,710.4,13,4.1,TRUE,57,Unsatisfied 270 | 369,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,24,Neutral 271 | 370,Female,31,New York,Gold,1140.6,15,4.5,TRUE,30,Satisfied 272 | 371,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,17,Neutral 273 | 372,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,42,Unsatisfied 274 | 373,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,11,Satisfied 275 | 374,Male,32,Miami,Silver,660.3,10,3.8,TRUE,36,Unsatisfied 276 | 375,Female,36,Houston,Bronze,470.5,8,3,FALSE,22,Neutral 277 | 376,Female,30,New York,Gold,1190.8,16,4.5,TRUE,23,Satisfied 278 | 377,Male,33,Los Angeles,Silver,770.2,11,4.2,FALSE,18,Neutral 279 | 378,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,43,Unsatisfied 280 | 379,Male,30,San Francisco,Gold,1450.5,19,4.6,FALSE,12,Satisfied 281 | 380,Male,27,Miami,Silver,710.4,13,4.1,TRUE,58,Unsatisfied 282 | 381,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,27,Neutral 283 | 382,Female,31,New York,Gold,1160.6,15,4.5,TRUE,31,Satisfied 284 | 383,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,15,Neutral 285 | 384,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,43,Unsatisfied 286 | 385,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,10,Satisfied 287 | 386,Male,32,Miami,Silver,660.3,10,3.8,TRUE,37,Unsatisfied 288 | 387,Female,36,Houston,Bronze,440.5,8,3.1,FALSE,23,Neutral 289 | 388,Female,30,New York,Gold,1200.8,16,4.7,TRUE,24,Satisfied 290 | 389,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,16,Neutral 291 | 390,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,44,Unsatisfied 292 | 391,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,13,Satisfied 293 | 392,Male,27,Miami,Silver,710.4,13,4.1,TRUE,59,Unsatisfied 294 | 393,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,28,Neutral 295 | 394,Female,31,New York,Gold,1140.6,15,4.5,TRUE,32,Satisfied 296 | 395,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,17,Neutral 297 | 396,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,44,Unsatisfied 298 | 397,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,12,Satisfied 299 | 398,Male,32,Miami,Silver,660.3,10,3.8,TRUE,38,Unsatisfied 300 | 399,Female,36,Houston,Bronze,470.5,8,3,FALSE,22,Neutral 301 | 400,Female,30,New York,Gold,1190.8,16,4.5,TRUE,25,Satisfied 302 | 401,Male,33,Los Angeles,Silver,800.2,11,4.1,FALSE,18,Neutral 303 | 402,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,45,Unsatisfied 304 | 403,Male,30,San Francisco,Gold,1450.5,19,4.6,FALSE,13,Satisfied 305 | 404,Male,27,Miami,Silver,710.4,13,4.1,TRUE,60,Unsatisfied 306 | 405,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,29,Neutral 307 | 406,Female,31,New York,Gold,1160.6,15,4.5,TRUE,33,Satisfied 308 | 407,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,16,Neutral 309 | 408,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,45,Unsatisfied 310 | 409,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,11,Satisfied 311 | 410,Male,32,Miami,Silver,660.3,10,3.8,TRUE,39,Unsatisfied 312 | 411,Female,36,Houston,Bronze,440.5,8,3.1,FALSE,24,Neutral 313 | 412,Female,30,New York,Gold,1200.8,16,4.7,TRUE,25,Satisfied 314 | 413,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,17,Neutral 315 | 414,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,46,Unsatisfied 316 | 415,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,14,Satisfied 317 | 416,Male,27,Miami,Silver,710.4,13,4.1,TRUE,61,Unsatisfied 318 | 417,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,30,Neutral 319 | 418,Female,31,New York,Gold,1140.6,15,4.5,TRUE,34,Satisfied 320 | 419,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,19,Neutral 321 | 420,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,46,Unsatisfied 322 | 421,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,12,Satisfied 323 | 422,Male,32,Miami,Silver,660.3,10,3.8,TRUE,40,Unsatisfied 324 | 423,Female,36,Houston,Bronze,470.5,8,3,FALSE,26,Neutral 325 | 424,Female,30,New York,Gold,1190.8,16,4.5,TRUE,27,Satisfied 326 | 425,Male,34,Los Angeles,Silver,780.2,11,4.2,FALSE,18,Neutral 327 | 426,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,47,Unsatisfied 328 | 427,Male,30,San Francisco,Gold,1450.5,19,4.6,FALSE,14,Satisfied 329 | 428,Male,27,Miami,Silver,710.4,13,4.1,TRUE,62,Unsatisfied 330 | 429,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,31,Neutral 331 | 430,Female,31,New York,Gold,1160.6,15,4.5,TRUE,35,Satisfied 332 | 431,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,20,Neutral 333 | 432,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,47,Unsatisfied 334 | 433,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,11,Satisfied 335 | 434,Male,32,Miami,Silver,660.3,10,3.8,TRUE,41,Unsatisfied 336 | 435,Female,36,Houston,Bronze,440.5,8,3.1,FALSE,28,Neutral 337 | 436,Female,30,New York,Gold,1200.8,16,4.7,TRUE,28,Satisfied 338 | 437,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,19,Neutral 339 | 438,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,48,Unsatisfied 340 | 439,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,15,Satisfied 341 | 440,Male,27,Miami,Silver,710.4,13,4.1,TRUE,63,Unsatisfied 342 | 441,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,32,Neutral 343 | 442,Female,31,New York,Gold,1140.6,15,4.5,TRUE,36,Satisfied 344 | 443,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,20,Neutral 345 | 444,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,48,Unsatisfied 346 | 445,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,13,Satisfied 347 | 446,Male,32,Miami,Silver,660.3,10,3.8,TRUE,42,Unsatisfied 348 | 447,Female,36,Houston,Bronze,470.5,8,3,FALSE,27,Neutral 349 | 448,Female,30,New York,Gold,1190.8,16,4.5,TRUE,28,Satisfied 350 | 449,Male,34,Los Angeles,Silver,780.2,11,4.2,FALSE,21,Neutral 351 | 450,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,49,Unsatisfied -------------------------------------------------------------------------------- /PandasAI-Agent/E-commerce-Customer-Behavior.csv: -------------------------------------------------------------------------------- 1 | Customer ID,Gender,Age,City,Membership Type,Total Spend,Items Purchased,Average Rating,Discount Applied,Days Since Last Purchase,Satisfaction Level 2 | 101,Female,29,New York,Gold,1120.2,14,4.6,TRUE,25,Satisfied 3 | 102,Male,34,Los Angeles,Silver,780.5,11,4.1,FALSE,18,Neutral 4 | 103,Female,43,Chicago,Bronze,510.75,9,3.4,TRUE,42,Unsatisfied 5 | 104,Male,30,San Francisco,Gold,1480.3,19,4.7,FALSE,12,Satisfied 6 | 105,Male,27,Miami,Silver,720.4,13,4,TRUE,55,Unsatisfied 7 | 106,Female,37,Houston,Bronze,440.8,8,3.1,FALSE,22,Neutral 8 | 107,Female,31,New York,Gold,1150.6,15,4.5,TRUE,28,Satisfied 9 | 108,Male,35,Los Angeles,Silver,800.9,12,4.2,FALSE,14,Neutral 10 | 109,Female,41,Chicago,Bronze,495.25,10,3.6,TRUE,40,Unsatisfied 11 | 110,Male,28,San Francisco,Gold,1520.1,21,4.8,FALSE,9,Satisfied 12 | 111,Male,32,Miami,Silver,690.3,11,3.8,TRUE,34,Unsatisfied 13 | 112,Female,36,Houston,Bronze,470.5,7,3.2,FALSE,20,Neutral 14 | 113,Female,30,New York,Gold,1200.8,16,4.3,TRUE,21,Satisfied 15 | 114,Male,33,Los Angeles,Silver,820.75,13,4.4,FALSE,15,Satisfied 16 | 115,Female,42,Chicago,Bronze,530.4,9,3.5,TRUE,38,Unsatisfied 17 | 116,Male,29,San Francisco,Gold,1360.2,18,4.9,FALSE,11,Satisfied 18 | 117,Male,26,Miami,Silver,700.6,12,3.7,TRUE,48,Unsatisfied 19 | 118,Female,38,Houston,Bronze,450.9,8,3,FALSE,25,Neutral 20 | 119,Female,32,New York,Gold,1170.3,14,4.7,TRUE,29,Satisfied 21 | 120,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,16,Neutral 22 | 121,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,41,Unsatisfied 23 | 122,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 24 | 123,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 25 | 124,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 26 | 125,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 27 | 126,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 28 | 127,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 29 | 128,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 30 | 129,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 31 | 130,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 32 | 131,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 33 | 132,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 34 | 133,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 35 | 134,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 36 | 135,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 37 | 136,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 38 | 137,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 39 | 138,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 40 | 139,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 41 | 140,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 42 | 141,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 43 | 142,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21,Neutral 44 | 143,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 45 | 144,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 46 | 145,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 47 | 146,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 48 | 147,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 49 | 148,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 50 | 149,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 51 | 150,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 52 | 151,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 53 | 152,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 54 | 153,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 55 | 154,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 56 | 155,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 57 | 156,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 58 | 157,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 59 | 158,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 60 | 159,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 61 | 160,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 62 | 161,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 63 | 162,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 64 | 163,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 65 | 164,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 66 | 165,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 67 | 166,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 68 | 167,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 69 | 168,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 70 | 169,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 71 | 170,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 72 | 171,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 73 | 172,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21, 74 | 173,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 75 | 174,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 76 | 175,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 77 | 176,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 78 | 177,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 79 | 178,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 80 | 179,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 81 | 180,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 82 | 181,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 83 | 182,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 84 | 183,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 85 | 184,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 86 | 185,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 87 | 186,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 88 | 187,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 89 | 188,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 90 | 189,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 91 | 190,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 92 | 191,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 93 | 192,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 94 | 193,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 95 | 194,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 96 | 195,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 97 | 196,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 98 | 197,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 99 | 198,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 100 | 199,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 101 | 200,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 102 | 201,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 103 | 202,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21,Neutral 104 | 203,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 105 | 204,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 106 | 205,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 107 | 206,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 108 | 207,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 109 | 208,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 110 | 209,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 111 | 210,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 112 | 211,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 113 | 212,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 114 | 213,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 115 | 214,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 116 | 215,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 117 | 216,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 118 | 217,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 119 | 218,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 120 | 219,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 121 | 220,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 122 | 221,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 123 | 222,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 124 | 223,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 125 | 224,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 126 | 225,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 127 | 226,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 128 | 227,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 129 | 228,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 130 | 229,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 131 | 230,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 132 | 231,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 133 | 232,Female,37,Houston,Bronze,420.8,7,3.1,FALSE,21,Neutral 134 | 233,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 135 | 234,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 136 | 235,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 137 | 236,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 138 | 237,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 139 | 238,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 140 | 239,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 141 | 240,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 142 | 241,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 143 | 242,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 144 | 243,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 145 | 244,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23, 146 | 245,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 147 | 246,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 148 | 247,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 149 | 248,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 150 | 249,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 151 | 250,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 152 | 251,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 153 | 252,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 154 | 253,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 155 | 254,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 156 | 255,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 157 | 256,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 158 | 257,Female,31,New York,Gold,1140.6,15,4.6,TRUE,27,Satisfied 159 | 258,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,13,Neutral 160 | 259,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 161 | 260,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 162 | 261,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 163 | 262,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 164 | 263,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 165 | 264,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 166 | 265,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 167 | 266,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 168 | 267,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 169 | 268,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 170 | 269,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 171 | 270,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 172 | 271,Female,43,Chicago,Bronze,500.75,10,3.3,TRUE,40,Unsatisfied 173 | 272,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,12,Satisfied 174 | 273,Male,27,Miami,Silver,700.4,13,4,TRUE,53,Unsatisfied 175 | 274,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 176 | 275,Female,31,New York,Gold,1140.6,15,4.5,TRUE,27,Satisfied 177 | 276,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,12,Neutral 178 | 277,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,38,Unsatisfied 179 | 278,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 180 | 279,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 181 | 280,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 182 | 281,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 183 | 282,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 184 | 283,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 185 | 284,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 186 | 285,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 187 | 286,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 188 | 287,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 189 | 288,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,13,Neutral 190 | 289,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 191 | 290,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 192 | 291,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 193 | 292,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 194 | 293,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 195 | 294,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 196 | 295,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 197 | 296,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 198 | 297,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 199 | 298,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 200 | 299,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 201 | 300,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 202 | 301,Male,36,San Francisco,Gold,1420.8,19,4.6,FALSE,11,Satisfied 203 | 302,Female,29,Miami,Silver,730.4,14,4,TRUE,50,Unsatisfied 204 | 303,Female,35,Houston,Bronze,450.6,7,3.3,FALSE,21,Neutral 205 | 304,Male,31,New York,Gold,1210.6,17,4.8,TRUE,18,Satisfied 206 | 305,Male,27,Los Angeles,Silver,780.9,11,4.2,FALSE,16,Neutral 207 | 306,Female,42,Chicago,Bronze,495.25,10,3.5,TRUE,35,Unsatisfied 208 | 307,Male,29,San Francisco,Gold,1390.2,18,4.7,FALSE,9,Satisfied 209 | 308,Male,26,Miami,Silver,670.6,12,3.9,TRUE,46,Unsatisfied 210 | 309,Female,38,Houston,Bronze,460.9,8,3.2,FALSE,25,Neutral 211 | 310,Female,32,New York,Gold,1170.3,14,4.5,TRUE,21,Satisfied 212 | 311,Male,34,Los Angeles,Silver,810.2,11,4,FALSE,14,Neutral 213 | 312,Female,43,Chicago,Bronze,515.75,9,3.3,TRUE,38,Unsatisfied 214 | 313,Male,30,San Francisco,Gold,1450.5,20,4.8,FALSE,12,Satisfied 215 | 314,Male,27,Miami,Silver,690.4,13,4.1,TRUE,52,Unsatisfied 216 | 315,Female,37,Houston,Bronze,420.8,7,3.4,FALSE,24,Neutral 217 | 316,Female,31,New York,Gold,1130.6,15,4.5,TRUE,26,Satisfied 218 | 317,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,13,Neutral 219 | 318,Female,41,Chicago,Bronze,480.25,9,3.6,TRUE,39,Unsatisfied 220 | 319,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,9,Satisfied 221 | 320,Male,32,Miami,Silver,680.3,10,3.8,TRUE,32,Unsatisfied 222 | 321,Female,36,Houston,Bronze,470.5,8,3,FALSE,18,Neutral 223 | 322,Female,30,New York,Gold,1180.8,16,4.7,TRUE,19,Satisfied 224 | 323,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 225 | 324,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,39,Unsatisfied 226 | 325,Male,30,San Francisco,Gold,1470.5,20,4.8,FALSE,13,Satisfied 227 | 326,Male,27,Miami,Silver,710.4,13,4.1,TRUE,54,Unsatisfied 228 | 327,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,23,Neutral 229 | 328,Female,31,New York,Gold,1140.6,15,4.5,TRUE,27,Satisfied 230 | 329,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,13,Neutral 231 | 330,Female,41,Chicago,Bronze,485.25,9,3.6,TRUE,39,Unsatisfied 232 | 331,Male,28,San Francisco,Gold,1500.1,21,4.9,FALSE,10,Satisfied 233 | 332,Male,32,Miami,Silver,670.3,10,3.8,TRUE,33,Unsatisfied 234 | 333,Female,36,Houston,Bronze,460.5,8,3.1,FALSE,19,Neutral 235 | 334,Female,30,New York,Gold,1190.8,16,4.5,TRUE,20,Satisfied 236 | 335,Male,33,Los Angeles,Silver,830.75,13,4.2,FALSE,14,Satisfied 237 | 336,Female,42,Chicago,Bronze,520.4,9,3.5,TRUE,37,Unsatisfied 238 | 337,Male,29,San Francisco,Gold,1370.2,18,4.7,FALSE,10,Satisfied 239 | 338,Male,26,Miami,Silver,690.6,12,3.9,TRUE,47,Unsatisfied 240 | 339,Female,38,Houston,Bronze,440.9,8,3.2,FALSE,24,Neutral 241 | 340,Female,32,New York,Gold,1160.3,14,4.4,TRUE,22,Satisfied 242 | 341,Male,34,Los Angeles,Silver,800.2,11,4.1,FALSE,17,Neutral 243 | 342,Female,43,Chicago,Bronze,525.75,9,3.3,TRUE,40,Unsatisfied 244 | 343,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,11,Satisfied 245 | 344,Male,27,Miami,Silver,720.4,13,4,TRUE,55,Unsatisfied 246 | 345,Female,37,Houston,Bronze,440.8,7,3.2,FALSE,25,Neutral 247 | 346,Female,31,New York,Gold,1150.6,15,4.5,TRUE,28,Satisfied 248 | 347,Male,35,Los Angeles,Silver,830.9,12,4.3,FALSE,14,Neutral 249 | 348,Female,41,Chicago,Bronze,490.25,9,3.6,TRUE,40,Unsatisfied 250 | 349,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,10,Satisfied 251 | 350,Male,32,Miami,Silver,690.3,10,3.8,TRUE,34,Unsatisfied 252 | 351,Female,36,Houston,Bronze,480.5,8,3.1,FALSE,20,Neutral 253 | 352,Female,30,New York,Gold,1170.8,16,4.7,TRUE,21,Satisfied 254 | 353,Male,34,Los Angeles,Silver,790.2,11,4,FALSE,15,Neutral 255 | 354,Female,43,Chicago,Bronze,510.75,10,3.3,TRUE,41,Unsatisfied 256 | 355,Male,30,San Francisco,Gold,1440.5,19,4.6,FALSE,12,Satisfied 257 | 356,Male,27,Miami,Silver,700.4,13,4.1,TRUE,56,Unsatisfied 258 | 357,Female,37,Houston,Bronze,410.8,7,3.4,FALSE,26,Neutral 259 | 358,Female,31,New York,Gold,1160.6,15,4.5,TRUE,29,Satisfied 260 | 359,Male,35,Los Angeles,Silver,810.9,12,4.3,FALSE,14,Neutral 261 | 360,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,41,Unsatisfied 262 | 361,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,10,Satisfied 263 | 362,Male,32,Miami,Silver,660.3,10,3.8,TRUE,35,Unsatisfied 264 | 363,Female,36,Houston,Bronze,430.5,8,3.1,FALSE,21,Neutral 265 | 364,Female,30,New York,Gold,1200.8,16,4.7,TRUE,22,Satisfied 266 | 365,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,16,Neutral 267 | 366,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,42,Unsatisfied 268 | 367,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,13,Satisfied 269 | 368,Male,27,Miami,Silver,710.4,13,4.1,TRUE,57,Unsatisfied 270 | 369,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,24,Neutral 271 | 370,Female,31,New York,Gold,1140.6,15,4.5,TRUE,30,Satisfied 272 | 371,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,17,Neutral 273 | 372,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,42,Unsatisfied 274 | 373,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,11,Satisfied 275 | 374,Male,32,Miami,Silver,660.3,10,3.8,TRUE,36,Unsatisfied 276 | 375,Female,36,Houston,Bronze,470.5,8,3,FALSE,22,Neutral 277 | 376,Female,30,New York,Gold,1190.8,16,4.5,TRUE,23,Satisfied 278 | 377,Male,33,Los Angeles,Silver,770.2,11,4.2,FALSE,18,Neutral 279 | 378,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,43,Unsatisfied 280 | 379,Male,30,San Francisco,Gold,1450.5,19,4.6,FALSE,12,Satisfied 281 | 380,Male,27,Miami,Silver,710.4,13,4.1,TRUE,58,Unsatisfied 282 | 381,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,27,Neutral 283 | 382,Female,31,New York,Gold,1160.6,15,4.5,TRUE,31,Satisfied 284 | 383,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,15,Neutral 285 | 384,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,43,Unsatisfied 286 | 385,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,10,Satisfied 287 | 386,Male,32,Miami,Silver,660.3,10,3.8,TRUE,37,Unsatisfied 288 | 387,Female,36,Houston,Bronze,440.5,8,3.1,FALSE,23,Neutral 289 | 388,Female,30,New York,Gold,1200.8,16,4.7,TRUE,24,Satisfied 290 | 389,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,16,Neutral 291 | 390,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,44,Unsatisfied 292 | 391,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,13,Satisfied 293 | 392,Male,27,Miami,Silver,710.4,13,4.1,TRUE,59,Unsatisfied 294 | 393,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,28,Neutral 295 | 394,Female,31,New York,Gold,1140.6,15,4.5,TRUE,32,Satisfied 296 | 395,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,17,Neutral 297 | 396,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,44,Unsatisfied 298 | 397,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,12,Satisfied 299 | 398,Male,32,Miami,Silver,660.3,10,3.8,TRUE,38,Unsatisfied 300 | 399,Female,36,Houston,Bronze,470.5,8,3,FALSE,22,Neutral 301 | 400,Female,30,New York,Gold,1190.8,16,4.5,TRUE,25,Satisfied 302 | 401,Male,33,Los Angeles,Silver,800.2,11,4.1,FALSE,18,Neutral 303 | 402,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,45,Unsatisfied 304 | 403,Male,30,San Francisco,Gold,1450.5,19,4.6,FALSE,13,Satisfied 305 | 404,Male,27,Miami,Silver,710.4,13,4.1,TRUE,60,Unsatisfied 306 | 405,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,29,Neutral 307 | 406,Female,31,New York,Gold,1160.6,15,4.5,TRUE,33,Satisfied 308 | 407,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,16,Neutral 309 | 408,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,45,Unsatisfied 310 | 409,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,11,Satisfied 311 | 410,Male,32,Miami,Silver,660.3,10,3.8,TRUE,39,Unsatisfied 312 | 411,Female,36,Houston,Bronze,440.5,8,3.1,FALSE,24,Neutral 313 | 412,Female,30,New York,Gold,1200.8,16,4.7,TRUE,25,Satisfied 314 | 413,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,17,Neutral 315 | 414,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,46,Unsatisfied 316 | 415,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,14,Satisfied 317 | 416,Male,27,Miami,Silver,710.4,13,4.1,TRUE,61,Unsatisfied 318 | 417,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,30,Neutral 319 | 418,Female,31,New York,Gold,1140.6,15,4.5,TRUE,34,Satisfied 320 | 419,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,19,Neutral 321 | 420,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,46,Unsatisfied 322 | 421,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,12,Satisfied 323 | 422,Male,32,Miami,Silver,660.3,10,3.8,TRUE,40,Unsatisfied 324 | 423,Female,36,Houston,Bronze,470.5,8,3,FALSE,26,Neutral 325 | 424,Female,30,New York,Gold,1190.8,16,4.5,TRUE,27,Satisfied 326 | 425,Male,34,Los Angeles,Silver,780.2,11,4.2,FALSE,18,Neutral 327 | 426,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,47,Unsatisfied 328 | 427,Male,30,San Francisco,Gold,1450.5,19,4.6,FALSE,14,Satisfied 329 | 428,Male,27,Miami,Silver,710.4,13,4.1,TRUE,62,Unsatisfied 330 | 429,Female,37,Houston,Bronze,430.8,7,3.4,FALSE,31,Neutral 331 | 430,Female,31,New York,Gold,1160.6,15,4.5,TRUE,35,Satisfied 332 | 431,Male,35,Los Angeles,Silver,820.9,12,4.3,FALSE,20,Neutral 333 | 432,Female,41,Chicago,Bronze,495.25,9,3.6,TRUE,47,Unsatisfied 334 | 433,Male,28,San Francisco,Gold,1490.1,21,4.9,FALSE,11,Satisfied 335 | 434,Male,32,Miami,Silver,660.3,10,3.8,TRUE,41,Unsatisfied 336 | 435,Female,36,Houston,Bronze,440.5,8,3.1,FALSE,28,Neutral 337 | 436,Female,30,New York,Gold,1200.8,16,4.7,TRUE,28,Satisfied 338 | 437,Male,34,Los Angeles,Silver,780.2,11,4.1,FALSE,19,Neutral 339 | 438,Female,43,Chicago,Bronze,505.75,10,3.3,TRUE,48,Unsatisfied 340 | 439,Male,30,San Francisco,Gold,1460.5,20,4.8,FALSE,15,Satisfied 341 | 440,Male,27,Miami,Silver,710.4,13,4.1,TRUE,63,Unsatisfied 342 | 441,Female,37,Houston,Bronze,450.8,7,3.4,FALSE,32,Neutral 343 | 442,Female,31,New York,Gold,1140.6,15,4.5,TRUE,36,Satisfied 344 | 443,Male,35,Los Angeles,Silver,800.9,12,4.1,FALSE,20,Neutral 345 | 444,Female,41,Chicago,Bronze,475.25,9,3.6,TRUE,48,Unsatisfied 346 | 445,Male,28,San Francisco,Gold,1480.1,21,4.9,FALSE,13,Satisfied 347 | 446,Male,32,Miami,Silver,660.3,10,3.8,TRUE,42,Unsatisfied 348 | 447,Female,36,Houston,Bronze,470.5,8,3,FALSE,27,Neutral 349 | 448,Female,30,New York,Gold,1190.8,16,4.5,TRUE,28,Satisfied 350 | 449,Male,34,Los Angeles,Silver,780.2,11,4.2,FALSE,21,Neutral 351 | 450,Female,43,Chicago,Bronze,515.75,10,3.3,TRUE,49,Unsatisfied -------------------------------------------------------------------------------- /Pandas-AI-with-Ollama.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "
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CountryPopulation
0United States339996563
1China1425671352
2Germany83294633
3Turkey85816199
4Japan123294513
\n", 61 | "
" 62 | ], 63 | "text/plain": [ 64 | " Country Population\n", 65 | "0 United States 339996563\n", 66 | "1 China 1425671352\n", 67 | "2 Germany 83294633\n", 68 | "3 Turkey 85816199\n", 69 | "4 Japan 123294513" 70 | ] 71 | }, 72 | "execution_count": 1, 73 | "metadata": {}, 74 | "output_type": "execute_result" 75 | } 76 | ], 77 | "source": [ 78 | "import pandas as pd\n", 79 | "data = pd.read_csv(\"population.csv\")\n", 80 | "data.head()" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 2, 86 | "metadata": {}, 87 | "outputs": [], 88 | "source": [ 89 | "from langchain_community.llms import Ollama\n", 90 | "llm = Ollama(model=\"mistral\")" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 3, 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [ 99 | "from pandasai import SmartDataframe\n", 100 | "df = SmartDataframe(data, config={\"llm\": llm})" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 4, 106 | "metadata": {}, 107 | "outputs": [ 108 | { 109 | "data": { 110 | "text/html": [ 111 | "
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CountryPopulation
\n", 136 | "
" 137 | ], 138 | "text/plain": [ 139 | "Empty DataFrame\n", 140 | "Columns: [Country, Population]\n", 141 | "Index: []" 142 | ] 143 | }, 144 | "execution_count": 4, 145 | "metadata": {}, 146 | "output_type": "execute_result" 147 | }, 148 | { 149 | "data": { 150 | "image/png": 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151 | "text/plain": [ 152 | "
" 153 | ] 154 | }, 155 | "metadata": {}, 156 | "output_type": "display_data" 157 | } 158 | ], 159 | "source": [ 160 | "df.chat('Which are top 5 countries by population?')" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": 5, 166 | "metadata": {}, 167 | "outputs": [ 168 | { 169 | "name": "stdout", 170 | "output_type": "stream", 171 | "text": [ 172 | "2058073260\n" 173 | ] 174 | } 175 | ], 176 | "source": [ 177 | "print(df.chat(\"What is the total populations of the top 5 countries by population?\"))" 178 | ] 179 | } 180 | ], 181 | "metadata": { 182 | "kernelspec": { 183 | "display_name": "genai", 184 | "language": "python", 185 | "name": "python3" 186 | }, 187 | "language_info": { 188 | "codemirror_mode": { 189 | "name": "ipython", 190 | "version": 3 191 | }, 192 | "file_extension": ".py", 193 | "mimetype": "text/x-python", 194 | "name": "python", 195 | "nbconvert_exporter": "python", 196 | "pygments_lexer": "ipython3", 197 | "version": "3.10.13" 198 | } 199 | }, 200 | "nbformat": 4, 201 | "nbformat_minor": 2 202 | } 203 | -------------------------------------------------------------------------------- /pandasai.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "data = pd.read_csv(\"population.csv\")" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "metadata": {}, 17 | "outputs": [ 18 | { 19 | "data": { 20 | "text/html": [ 21 | "
\n", 22 | "\n", 35 | "\n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | "
CountryPopulation
0United States339996563
1China1425671352
2Germany83294633
3Turkey85816199
4Japan123294513
\n", 71 | "
" 72 | ], 73 | "text/plain": [ 74 | " Country Population\n", 75 | "0 United States 339996563\n", 76 | "1 China 1425671352\n", 77 | "2 Germany 83294633\n", 78 | "3 Turkey 85816199\n", 79 | "4 Japan 123294513" 80 | ] 81 | }, 82 | "execution_count": 2, 83 | "metadata": {}, 84 | "output_type": "execute_result" 85 | } 86 | ], 87 | "source": [ 88 | "data.head()" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 3, 94 | "metadata": {}, 95 | "outputs": [], 96 | "source": [ 97 | "from langchain_community.llms import Ollama \n", 98 | "\n", 99 | "llm = Ollama(model=\"mistral\")" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 4, 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [ 108 | "from pandasai import SmartDataframe \n", 109 | "\n", 110 | "df = SmartDataframe(data, config={\"llm\": llm})" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": 5, 116 | "metadata": {}, 117 | "outputs": [ 118 | { 119 | "name": "stdout", 120 | "output_type": "stream", 121 | "text": [ 122 | "Top 5 countries by population:\n", 123 | " Country Population\n", 124 | "8 India 1428627663\n", 125 | "1 China 1425671352\n", 126 | "0 United States 339996563\n", 127 | "5 Indonesia 277534122\n", 128 | "6 Pakistan 240485658\n" 129 | ] 130 | }, 131 | { 132 | "data": { 133 | "text/html": [ 134 | "
\n", 135 | "\n", 148 | "\n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | "
CountryPopulation
8India1428627663
1China1425671352
0United States339996563
5Indonesia277534122
6Pakistan240485658
\n", 184 | "
" 185 | ], 186 | "text/plain": [ 187 | " Country Population\n", 188 | "8 India 1428627663\n", 189 | "1 China 1425671352\n", 190 | "0 United States 339996563\n", 191 | "5 Indonesia 277534122\n", 192 | "6 Pakistan 240485658" 193 | ] 194 | }, 195 | "execution_count": 5, 196 | "metadata": {}, 197 | "output_type": "execute_result" 198 | }, 199 | { 200 | "data": { 201 | "image/png": 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", 202 | "text/plain": [ 203 | "
" 204 | ] 205 | }, 206 | "metadata": {}, 207 | "output_type": "display_data" 208 | } 209 | ], 210 | "source": [ 211 | "df.chat(\"Which are the top 5 countries by population\")" 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": 6, 217 | "metadata": {}, 218 | "outputs": [ 219 | { 220 | "data": { 221 | "text/plain": [ 222 | "2058073260" 223 | ] 224 | }, 225 | "execution_count": 6, 226 | "metadata": {}, 227 | "output_type": "execute_result" 228 | } 229 | ], 230 | "source": [ 231 | "df.chat(\"What is the total populations of the top 5 countries by population?\")" 232 | ] 233 | } 234 | ], 235 | "metadata": { 236 | "kernelspec": { 237 | "display_name": "genai", 238 | "language": "python", 239 | "name": "python3" 240 | }, 241 | "language_info": { 242 | "codemirror_mode": { 243 | "name": "ipython", 244 | "version": 3 245 | }, 246 | "file_extension": ".py", 247 | "mimetype": "text/x-python", 248 | "name": "python", 249 | "nbconvert_exporter": "python", 250 | "pygments_lexer": "ipython3", 251 | "version": "3.10.13" 252 | } 253 | }, 254 | "nbformat": 4, 255 | "nbformat_minor": 2 256 | } 257 | -------------------------------------------------------------------------------- /Datasets/titanic.csv: -------------------------------------------------------------------------------- 1 | survived,pclass,sex,age,sibsp,parch,fare,embarked,class,who,adult_male,deck,embark_town,alive,alone 2 | 0,3,male,22.0,1,0,7.25,S,Third,man,True,,Southampton,no,False 3 | 1,1,female,38.0,1,0,71.2833,C,First,woman,False,C,Cherbourg,yes,False 4 | 1,3,female,26.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True 5 | 1,1,female,35.0,1,0,53.1,S,First,woman,False,C,Southampton,yes,False 6 | 0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 7 | 0,3,male,,0,0,8.4583,Q,Third,man,True,,Queenstown,no,True 8 | 0,1,male,54.0,0,0,51.8625,S,First,man,True,E,Southampton,no,True 9 | 0,3,male,2.0,3,1,21.075,S,Third,child,False,,Southampton,no,False 10 | 1,3,female,27.0,0,2,11.1333,S,Third,woman,False,,Southampton,yes,False 11 | 1,2,female,14.0,1,0,30.0708,C,Second,child,False,,Cherbourg,yes,False 12 | 1,3,female,4.0,1,1,16.7,S,Third,child,False,G,Southampton,yes,False 13 | 1,1,female,58.0,0,0,26.55,S,First,woman,False,C,Southampton,yes,True 14 | 0,3,male,20.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 15 | 0,3,male,39.0,1,5,31.275,S,Third,man,True,,Southampton,no,False 16 | 0,3,female,14.0,0,0,7.8542,S,Third,child,False,,Southampton,no,True 17 | 1,2,female,55.0,0,0,16.0,S,Second,woman,False,,Southampton,yes,True 18 | 0,3,male,2.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 19 | 1,2,male,,0,0,13.0,S,Second,man,True,,Southampton,yes,True 20 | 0,3,female,31.0,1,0,18.0,S,Third,woman,False,,Southampton,no,False 21 | 1,3,female,,0,0,7.225,C,Third,woman,False,,Cherbourg,yes,True 22 | 0,2,male,35.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 23 | 1,2,male,34.0,0,0,13.0,S,Second,man,True,D,Southampton,yes,True 24 | 1,3,female,15.0,0,0,8.0292,Q,Third,child,False,,Queenstown,yes,True 25 | 1,1,male,28.0,0,0,35.5,S,First,man,True,A,Southampton,yes,True 26 | 0,3,female,8.0,3,1,21.075,S,Third,child,False,,Southampton,no,False 27 | 1,3,female,38.0,1,5,31.3875,S,Third,woman,False,,Southampton,yes,False 28 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 29 | 0,1,male,19.0,3,2,263.0,S,First,man,True,C,Southampton,no,False 30 | 1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 31 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 32 | 0,1,male,40.0,0,0,27.7208,C,First,man,True,,Cherbourg,no,True 33 | 1,1,female,,1,0,146.5208,C,First,woman,False,B,Cherbourg,yes,False 34 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 35 | 0,2,male,66.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 36 | 0,1,male,28.0,1,0,82.1708,C,First,man,True,,Cherbourg,no,False 37 | 0,1,male,42.0,1,0,52.0,S,First,man,True,,Southampton,no,False 38 | 1,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,yes,True 39 | 0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 40 | 0,3,female,18.0,2,0,18.0,S,Third,woman,False,,Southampton,no,False 41 | 1,3,female,14.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False 42 | 0,3,female,40.0,1,0,9.475,S,Third,woman,False,,Southampton,no,False 43 | 0,2,female,27.0,1,0,21.0,S,Second,woman,False,,Southampton,no,False 44 | 0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 45 | 1,2,female,3.0,1,2,41.5792,C,Second,child,False,,Cherbourg,yes,False 46 | 1,3,female,19.0,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 47 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 48 | 0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False 49 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 50 | 0,3,male,,2,0,21.6792,C,Third,man,True,,Cherbourg,no,False 51 | 0,3,female,18.0,1,0,17.8,S,Third,woman,False,,Southampton,no,False 52 | 0,3,male,7.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 53 | 0,3,male,21.0,0,0,7.8,S,Third,man,True,,Southampton,no,True 54 | 1,1,female,49.0,1,0,76.7292,C,First,woman,False,D,Cherbourg,yes,False 55 | 1,2,female,29.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 56 | 0,1,male,65.0,0,1,61.9792,C,First,man,True,B,Cherbourg,no,False 57 | 1,1,male,,0,0,35.5,S,First,man,True,C,Southampton,yes,True 58 | 1,2,female,21.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 59 | 0,3,male,28.5,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 60 | 1,2,female,5.0,1,2,27.75,S,Second,child,False,,Southampton,yes,False 61 | 0,3,male,11.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 62 | 0,3,male,22.0,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 63 | 1,1,female,38.0,0,0,80.0,,First,woman,False,B,,yes,True 64 | 0,1,male,45.0,1,0,83.475,S,First,man,True,C,Southampton,no,False 65 | 0,3,male,4.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 66 | 0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True 67 | 1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False 68 | 1,2,female,29.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True 69 | 0,3,male,19.0,0,0,8.1583,S,Third,man,True,,Southampton,no,True 70 | 1,3,female,17.0,4,2,7.925,S,Third,woman,False,,Southampton,yes,False 71 | 0,3,male,26.0,2,0,8.6625,S,Third,man,True,,Southampton,no,False 72 | 0,2,male,32.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 73 | 0,3,female,16.0,5,2,46.9,S,Third,woman,False,,Southampton,no,False 74 | 0,2,male,21.0,0,0,73.5,S,Second,man,True,,Southampton,no,True 75 | 0,3,male,26.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False 76 | 1,3,male,32.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 77 | 0,3,male,25.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True 78 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 79 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 80 | 1,2,male,0.83,0,2,29.0,S,Second,child,False,,Southampton,yes,False 81 | 1,3,female,30.0,0,0,12.475,S,Third,woman,False,,Southampton,yes,True 82 | 0,3,male,22.0,0,0,9.0,S,Third,man,True,,Southampton,no,True 83 | 1,3,male,29.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True 84 | 1,3,female,,0,0,7.7875,Q,Third,woman,False,,Queenstown,yes,True 85 | 0,1,male,28.0,0,0,47.1,S,First,man,True,,Southampton,no,True 86 | 1,2,female,17.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 87 | 1,3,female,33.0,3,0,15.85,S,Third,woman,False,,Southampton,yes,False 88 | 0,3,male,16.0,1,3,34.375,S,Third,man,True,,Southampton,no,False 89 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 90 | 1,1,female,23.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False 91 | 0,3,male,24.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 92 | 0,3,male,29.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 93 | 0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 94 | 0,1,male,46.0,1,0,61.175,S,First,man,True,E,Southampton,no,False 95 | 0,3,male,26.0,1,2,20.575,S,Third,man,True,,Southampton,no,False 96 | 0,3,male,59.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 97 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 98 | 0,1,male,71.0,0,0,34.6542,C,First,man,True,A,Cherbourg,no,True 99 | 1,1,male,23.0,0,1,63.3583,C,First,man,True,D,Cherbourg,yes,False 100 | 1,2,female,34.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False 101 | 0,2,male,34.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 102 | 0,3,female,28.0,0,0,7.8958,S,Third,woman,False,,Southampton,no,True 103 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 104 | 0,1,male,21.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False 105 | 0,3,male,33.0,0,0,8.6542,S,Third,man,True,,Southampton,no,True 106 | 0,3,male,37.0,2,0,7.925,S,Third,man,True,,Southampton,no,False 107 | 0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 108 | 1,3,female,21.0,0,0,7.65,S,Third,woman,False,,Southampton,yes,True 109 | 1,3,male,,0,0,7.775,S,Third,man,True,,Southampton,yes,True 110 | 0,3,male,38.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 111 | 1,3,female,,1,0,24.15,Q,Third,woman,False,,Queenstown,yes,False 112 | 0,1,male,47.0,0,0,52.0,S,First,man,True,C,Southampton,no,True 113 | 0,3,female,14.5,1,0,14.4542,C,Third,child,False,,Cherbourg,no,False 114 | 0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 115 | 0,3,female,20.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False 116 | 0,3,female,17.0,0,0,14.4583,C,Third,woman,False,,Cherbourg,no,True 117 | 0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 118 | 0,3,male,70.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 119 | 0,2,male,29.0,1,0,21.0,S,Second,man,True,,Southampton,no,False 120 | 0,1,male,24.0,0,1,247.5208,C,First,man,True,B,Cherbourg,no,False 121 | 0,3,female,2.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 122 | 0,2,male,21.0,2,0,73.5,S,Second,man,True,,Southampton,no,False 123 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 124 | 0,2,male,32.5,1,0,30.0708,C,Second,man,True,,Cherbourg,no,False 125 | 1,2,female,32.5,0,0,13.0,S,Second,woman,False,E,Southampton,yes,True 126 | 0,1,male,54.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False 127 | 1,3,male,12.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False 128 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 129 | 1,3,male,24.0,0,0,7.1417,S,Third,man,True,,Southampton,yes,True 130 | 1,3,female,,1,1,22.3583,C,Third,woman,False,F,Cherbourg,yes,False 131 | 0,3,male,45.0,0,0,6.975,S,Third,man,True,,Southampton,no,True 132 | 0,3,male,33.0,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 133 | 0,3,male,20.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 134 | 0,3,female,47.0,1,0,14.5,S,Third,woman,False,,Southampton,no,False 135 | 1,2,female,29.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 136 | 0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 137 | 0,2,male,23.0,0,0,15.0458,C,Second,man,True,,Cherbourg,no,True 138 | 1,1,female,19.0,0,2,26.2833,S,First,woman,False,D,Southampton,yes,False 139 | 0,1,male,37.0,1,0,53.1,S,First,man,True,C,Southampton,no,False 140 | 0,3,male,16.0,0,0,9.2167,S,Third,man,True,,Southampton,no,True 141 | 0,1,male,24.0,0,0,79.2,C,First,man,True,B,Cherbourg,no,True 142 | 0,3,female,,0,2,15.2458,C,Third,woman,False,,Cherbourg,no,False 143 | 1,3,female,22.0,0,0,7.75,S,Third,woman,False,,Southampton,yes,True 144 | 1,3,female,24.0,1,0,15.85,S,Third,woman,False,,Southampton,yes,False 145 | 0,3,male,19.0,0,0,6.75,Q,Third,man,True,,Queenstown,no,True 146 | 0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True 147 | 0,2,male,19.0,1,1,36.75,S,Second,man,True,,Southampton,no,False 148 | 1,3,male,27.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True 149 | 0,3,female,9.0,2,2,34.375,S,Third,child,False,,Southampton,no,False 150 | 0,2,male,36.5,0,2,26.0,S,Second,man,True,F,Southampton,no,False 151 | 0,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 152 | 0,2,male,51.0,0,0,12.525,S,Second,man,True,,Southampton,no,True 153 | 1,1,female,22.0,1,0,66.6,S,First,woman,False,C,Southampton,yes,False 154 | 0,3,male,55.5,0,0,8.05,S,Third,man,True,,Southampton,no,True 155 | 0,3,male,40.5,0,2,14.5,S,Third,man,True,,Southampton,no,False 156 | 0,3,male,,0,0,7.3125,S,Third,man,True,,Southampton,no,True 157 | 0,1,male,51.0,0,1,61.3792,C,First,man,True,,Cherbourg,no,False 158 | 1,3,female,16.0,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True 159 | 0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 160 | 0,3,male,,0,0,8.6625,S,Third,man,True,,Southampton,no,True 161 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 162 | 0,3,male,44.0,0,1,16.1,S,Third,man,True,,Southampton,no,False 163 | 1,2,female,40.0,0,0,15.75,S,Second,woman,False,,Southampton,yes,True 164 | 0,3,male,26.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 165 | 0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 166 | 0,3,male,1.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 167 | 1,3,male,9.0,0,2,20.525,S,Third,child,False,,Southampton,yes,False 168 | 1,1,female,,0,1,55.0,S,First,woman,False,E,Southampton,yes,False 169 | 0,3,female,45.0,1,4,27.9,S,Third,woman,False,,Southampton,no,False 170 | 0,1,male,,0,0,25.925,S,First,man,True,,Southampton,no,True 171 | 0,3,male,28.0,0,0,56.4958,S,Third,man,True,,Southampton,no,True 172 | 0,1,male,61.0,0,0,33.5,S,First,man,True,B,Southampton,no,True 173 | 0,3,male,4.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 174 | 1,3,female,1.0,1,1,11.1333,S,Third,child,False,,Southampton,yes,False 175 | 0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 176 | 0,1,male,56.0,0,0,30.6958,C,First,man,True,A,Cherbourg,no,True 177 | 0,3,male,18.0,1,1,7.8542,S,Third,man,True,,Southampton,no,False 178 | 0,3,male,,3,1,25.4667,S,Third,man,True,,Southampton,no,False 179 | 0,1,female,50.0,0,0,28.7125,C,First,woman,False,C,Cherbourg,no,True 180 | 0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 181 | 0,3,male,36.0,0,0,0.0,S,Third,man,True,,Southampton,no,True 182 | 0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False 183 | 0,2,male,,0,0,15.05,C,Second,man,True,,Cherbourg,no,True 184 | 0,3,male,9.0,4,2,31.3875,S,Third,child,False,,Southampton,no,False 185 | 1,2,male,1.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False 186 | 1,3,female,4.0,0,2,22.025,S,Third,child,False,,Southampton,yes,False 187 | 0,1,male,,0,0,50.0,S,First,man,True,A,Southampton,no,True 188 | 1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False 189 | 1,1,male,45.0,0,0,26.55,S,First,man,True,,Southampton,yes,True 190 | 0,3,male,40.0,1,1,15.5,Q,Third,man,True,,Queenstown,no,False 191 | 0,3,male,36.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 192 | 1,2,female,32.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 193 | 0,2,male,19.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 194 | 1,3,female,19.0,1,0,7.8542,S,Third,woman,False,,Southampton,yes,False 195 | 1,2,male,3.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False 196 | 1,1,female,44.0,0,0,27.7208,C,First,woman,False,B,Cherbourg,yes,True 197 | 1,1,female,58.0,0,0,146.5208,C,First,woman,False,B,Cherbourg,yes,True 198 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 199 | 0,3,male,42.0,0,1,8.4042,S,Third,man,True,,Southampton,no,False 200 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 201 | 0,2,female,24.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True 202 | 0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 203 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 204 | 0,3,male,34.0,0,0,6.4958,S,Third,man,True,,Southampton,no,True 205 | 0,3,male,45.5,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 206 | 1,3,male,18.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 207 | 0,3,female,2.0,0,1,10.4625,S,Third,child,False,G,Southampton,no,False 208 | 0,3,male,32.0,1,0,15.85,S,Third,man,True,,Southampton,no,False 209 | 1,3,male,26.0,0,0,18.7875,C,Third,man,True,,Cherbourg,yes,True 210 | 1,3,female,16.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 211 | 1,1,male,40.0,0,0,31.0,C,First,man,True,A,Cherbourg,yes,True 212 | 0,3,male,24.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 213 | 1,2,female,35.0,0,0,21.0,S,Second,woman,False,,Southampton,yes,True 214 | 0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 215 | 0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 216 | 0,3,male,,1,0,7.75,Q,Third,man,True,,Queenstown,no,False 217 | 1,1,female,31.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False 218 | 1,3,female,27.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True 219 | 0,2,male,42.0,1,0,27.0,S,Second,man,True,,Southampton,no,False 220 | 1,1,female,32.0,0,0,76.2917,C,First,woman,False,D,Cherbourg,yes,True 221 | 0,2,male,30.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 222 | 1,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 223 | 0,2,male,27.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 224 | 0,3,male,51.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 225 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 226 | 1,1,male,38.0,1,0,90.0,S,First,man,True,C,Southampton,yes,False 227 | 0,3,male,22.0,0,0,9.35,S,Third,man,True,,Southampton,no,True 228 | 1,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True 229 | 0,3,male,20.5,0,0,7.25,S,Third,man,True,,Southampton,no,True 230 | 0,2,male,18.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 231 | 0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False 232 | 1,1,female,35.0,1,0,83.475,S,First,woman,False,C,Southampton,yes,False 233 | 0,3,male,29.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 234 | 0,2,male,59.0,0,0,13.5,S,Second,man,True,,Southampton,no,True 235 | 1,3,female,5.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False 236 | 0,2,male,24.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 237 | 0,3,female,,0,0,7.55,S,Third,woman,False,,Southampton,no,True 238 | 0,2,male,44.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 239 | 1,2,female,8.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False 240 | 0,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 241 | 0,2,male,33.0,0,0,12.275,S,Second,man,True,,Southampton,no,True 242 | 0,3,female,,1,0,14.4542,C,Third,woman,False,,Cherbourg,no,False 243 | 1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False 244 | 0,2,male,29.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 245 | 0,3,male,22.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 246 | 0,3,male,30.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 247 | 0,1,male,44.0,2,0,90.0,Q,First,man,True,C,Queenstown,no,False 248 | 0,3,female,25.0,0,0,7.775,S,Third,woman,False,,Southampton,no,True 249 | 1,2,female,24.0,0,2,14.5,S,Second,woman,False,,Southampton,yes,False 250 | 1,1,male,37.0,1,1,52.5542,S,First,man,True,D,Southampton,yes,False 251 | 0,2,male,54.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 252 | 0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True 253 | 0,3,female,29.0,1,1,10.4625,S,Third,woman,False,G,Southampton,no,False 254 | 0,1,male,62.0,0,0,26.55,S,First,man,True,C,Southampton,no,True 255 | 0,3,male,30.0,1,0,16.1,S,Third,man,True,,Southampton,no,False 256 | 0,3,female,41.0,0,2,20.2125,S,Third,woman,False,,Southampton,no,False 257 | 1,3,female,29.0,0,2,15.2458,C,Third,woman,False,,Cherbourg,yes,False 258 | 1,1,female,,0,0,79.2,C,First,woman,False,,Cherbourg,yes,True 259 | 1,1,female,30.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True 260 | 1,1,female,35.0,0,0,512.3292,C,First,woman,False,,Cherbourg,yes,True 261 | 1,2,female,50.0,0,1,26.0,S,Second,woman,False,,Southampton,yes,False 262 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 263 | 1,3,male,3.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False 264 | 0,1,male,52.0,1,1,79.65,S,First,man,True,E,Southampton,no,False 265 | 0,1,male,40.0,0,0,0.0,S,First,man,True,B,Southampton,no,True 266 | 0,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True 267 | 0,2,male,36.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 268 | 0,3,male,16.0,4,1,39.6875,S,Third,man,True,,Southampton,no,False 269 | 1,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,yes,False 270 | 1,1,female,58.0,0,1,153.4625,S,First,woman,False,C,Southampton,yes,False 271 | 1,1,female,35.0,0,0,135.6333,S,First,woman,False,C,Southampton,yes,True 272 | 0,1,male,,0,0,31.0,S,First,man,True,,Southampton,no,True 273 | 1,3,male,25.0,0,0,0.0,S,Third,man,True,,Southampton,yes,True 274 | 1,2,female,41.0,0,1,19.5,S,Second,woman,False,,Southampton,yes,False 275 | 0,1,male,37.0,0,1,29.7,C,First,man,True,C,Cherbourg,no,False 276 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 277 | 1,1,female,63.0,1,0,77.9583,S,First,woman,False,D,Southampton,yes,False 278 | 0,3,female,45.0,0,0,7.75,S,Third,woman,False,,Southampton,no,True 279 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 280 | 0,3,male,7.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 281 | 1,3,female,35.0,1,1,20.25,S,Third,woman,False,,Southampton,yes,False 282 | 0,3,male,65.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 283 | 0,3,male,28.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 284 | 0,3,male,16.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 285 | 1,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 286 | 0,1,male,,0,0,26.0,S,First,man,True,A,Southampton,no,True 287 | 0,3,male,33.0,0,0,8.6625,C,Third,man,True,,Cherbourg,no,True 288 | 1,3,male,30.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True 289 | 0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 290 | 1,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True 291 | 1,3,female,22.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 292 | 1,1,female,26.0,0,0,78.85,S,First,woman,False,,Southampton,yes,True 293 | 1,1,female,19.0,1,0,91.0792,C,First,woman,False,B,Cherbourg,yes,False 294 | 0,2,male,36.0,0,0,12.875,C,Second,man,True,D,Cherbourg,no,True 295 | 0,3,female,24.0,0,0,8.85,S,Third,woman,False,,Southampton,no,True 296 | 0,3,male,24.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 297 | 0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True 298 | 0,3,male,23.5,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 299 | 0,1,female,2.0,1,2,151.55,S,First,child,False,C,Southampton,no,False 300 | 1,1,male,,0,0,30.5,S,First,man,True,C,Southampton,yes,True 301 | 1,1,female,50.0,0,1,247.5208,C,First,woman,False,B,Cherbourg,yes,False 302 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 303 | 1,3,male,,2,0,23.25,Q,Third,man,True,,Queenstown,yes,False 304 | 0,3,male,19.0,0,0,0.0,S,Third,man,True,,Southampton,no,True 305 | 1,2,female,,0,0,12.35,Q,Second,woman,False,E,Queenstown,yes,True 306 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 307 | 1,1,male,0.92,1,2,151.55,S,First,child,False,C,Southampton,yes,False 308 | 1,1,female,,0,0,110.8833,C,First,woman,False,,Cherbourg,yes,True 309 | 1,1,female,17.0,1,0,108.9,C,First,woman,False,C,Cherbourg,yes,False 310 | 0,2,male,30.0,1,0,24.0,C,Second,man,True,,Cherbourg,no,False 311 | 1,1,female,30.0,0,0,56.9292,C,First,woman,False,E,Cherbourg,yes,True 312 | 1,1,female,24.0,0,0,83.1583,C,First,woman,False,C,Cherbourg,yes,True 313 | 1,1,female,18.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False 314 | 0,2,female,26.0,1,1,26.0,S,Second,woman,False,,Southampton,no,False 315 | 0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 316 | 0,2,male,43.0,1,1,26.25,S,Second,man,True,,Southampton,no,False 317 | 1,3,female,26.0,0,0,7.8542,S,Third,woman,False,,Southampton,yes,True 318 | 1,2,female,24.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 319 | 0,2,male,54.0,0,0,14.0,S,Second,man,True,,Southampton,no,True 320 | 1,1,female,31.0,0,2,164.8667,S,First,woman,False,C,Southampton,yes,False 321 | 1,1,female,40.0,1,1,134.5,C,First,woman,False,E,Cherbourg,yes,False 322 | 0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 323 | 0,3,male,27.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 324 | 1,2,female,30.0,0,0,12.35,Q,Second,woman,False,,Queenstown,yes,True 325 | 1,2,female,22.0,1,1,29.0,S,Second,woman,False,,Southampton,yes,False 326 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 327 | 1,1,female,36.0,0,0,135.6333,C,First,woman,False,C,Cherbourg,yes,True 328 | 0,3,male,61.0,0,0,6.2375,S,Third,man,True,,Southampton,no,True 329 | 1,2,female,36.0,0,0,13.0,S,Second,woman,False,D,Southampton,yes,True 330 | 1,3,female,31.0,1,1,20.525,S,Third,woman,False,,Southampton,yes,False 331 | 1,1,female,16.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False 332 | 1,3,female,,2,0,23.25,Q,Third,woman,False,,Queenstown,yes,False 333 | 0,1,male,45.5,0,0,28.5,S,First,man,True,C,Southampton,no,True 334 | 0,1,male,38.0,0,1,153.4625,S,First,man,True,C,Southampton,no,False 335 | 0,3,male,16.0,2,0,18.0,S,Third,man,True,,Southampton,no,False 336 | 1,1,female,,1,0,133.65,S,First,woman,False,,Southampton,yes,False 337 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 338 | 0,1,male,29.0,1,0,66.6,S,First,man,True,C,Southampton,no,False 339 | 1,1,female,41.0,0,0,134.5,C,First,woman,False,E,Cherbourg,yes,True 340 | 1,3,male,45.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 341 | 0,1,male,45.0,0,0,35.5,S,First,man,True,,Southampton,no,True 342 | 1,2,male,2.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False 343 | 1,1,female,24.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False 344 | 0,2,male,28.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 345 | 0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 346 | 0,2,male,36.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 347 | 1,2,female,24.0,0,0,13.0,S,Second,woman,False,F,Southampton,yes,True 348 | 1,2,female,40.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 349 | 1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False 350 | 1,3,male,3.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False 351 | 0,3,male,42.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 352 | 0,3,male,23.0,0,0,9.225,S,Third,man,True,,Southampton,no,True 353 | 0,1,male,,0,0,35.0,S,First,man,True,C,Southampton,no,True 354 | 0,3,male,15.0,1,1,7.2292,C,Third,child,False,,Cherbourg,no,False 355 | 0,3,male,25.0,1,0,17.8,S,Third,man,True,,Southampton,no,False 356 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 357 | 0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 358 | 1,1,female,22.0,0,1,55.0,S,First,woman,False,E,Southampton,yes,False 359 | 0,2,female,38.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True 360 | 1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 361 | 1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 362 | 0,3,male,40.0,1,4,27.9,S,Third,man,True,,Southampton,no,False 363 | 0,2,male,29.0,1,0,27.7208,C,Second,man,True,,Cherbourg,no,False 364 | 0,3,female,45.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False 365 | 0,3,male,35.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 366 | 0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False 367 | 0,3,male,30.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 368 | 1,1,female,60.0,1,0,75.25,C,First,woman,False,D,Cherbourg,yes,False 369 | 1,3,female,,0,0,7.2292,C,Third,woman,False,,Cherbourg,yes,True 370 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 371 | 1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True 372 | 1,1,male,25.0,1,0,55.4417,C,First,man,True,E,Cherbourg,yes,False 373 | 0,3,male,18.0,1,0,6.4958,S,Third,man,True,,Southampton,no,False 374 | 0,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 375 | 0,1,male,22.0,0,0,135.6333,C,First,man,True,,Cherbourg,no,True 376 | 0,3,female,3.0,3,1,21.075,S,Third,child,False,,Southampton,no,False 377 | 1,1,female,,1,0,82.1708,C,First,woman,False,,Cherbourg,yes,False 378 | 1,3,female,22.0,0,0,7.25,S,Third,woman,False,,Southampton,yes,True 379 | 0,1,male,27.0,0,2,211.5,C,First,man,True,C,Cherbourg,no,False 380 | 0,3,male,20.0,0,0,4.0125,C,Third,man,True,,Cherbourg,no,True 381 | 0,3,male,19.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 382 | 1,1,female,42.0,0,0,227.525,C,First,woman,False,,Cherbourg,yes,True 383 | 1,3,female,1.0,0,2,15.7417,C,Third,child,False,,Cherbourg,yes,False 384 | 0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 385 | 1,1,female,35.0,1,0,52.0,S,First,woman,False,,Southampton,yes,False 386 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 387 | 0,2,male,18.0,0,0,73.5,S,Second,man,True,,Southampton,no,True 388 | 0,3,male,1.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 389 | 1,2,female,36.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 390 | 0,3,male,,0,0,7.7292,Q,Third,man,True,,Queenstown,no,True 391 | 1,2,female,17.0,0,0,12.0,C,Second,woman,False,,Cherbourg,yes,True 392 | 1,1,male,36.0,1,2,120.0,S,First,man,True,B,Southampton,yes,False 393 | 1,3,male,21.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True 394 | 0,3,male,28.0,2,0,7.925,S,Third,man,True,,Southampton,no,False 395 | 1,1,female,23.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False 396 | 1,3,female,24.0,0,2,16.7,S,Third,woman,False,G,Southampton,yes,False 397 | 0,3,male,22.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 398 | 0,3,female,31.0,0,0,7.8542,S,Third,woman,False,,Southampton,no,True 399 | 0,2,male,46.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 400 | 0,2,male,23.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 401 | 1,2,female,28.0,0,0,12.65,S,Second,woman,False,,Southampton,yes,True 402 | 1,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 403 | 0,3,male,26.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 404 | 0,3,female,21.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False 405 | 0,3,male,28.0,1,0,15.85,S,Third,man,True,,Southampton,no,False 406 | 0,3,female,20.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True 407 | 0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False 408 | 0,3,male,51.0,0,0,7.75,S,Third,man,True,,Southampton,no,True 409 | 1,2,male,3.0,1,1,18.75,S,Second,child,False,,Southampton,yes,False 410 | 0,3,male,21.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 411 | 0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False 412 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 413 | 0,3,male,,0,0,6.8583,Q,Third,man,True,,Queenstown,no,True 414 | 1,1,female,33.0,1,0,90.0,Q,First,woman,False,C,Queenstown,yes,False 415 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 416 | 1,3,male,44.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 417 | 0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True 418 | 1,2,female,34.0,1,1,32.5,S,Second,woman,False,,Southampton,yes,False 419 | 1,2,female,18.0,0,2,13.0,S,Second,woman,False,,Southampton,yes,False 420 | 0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 421 | 0,3,female,10.0,0,2,24.15,S,Third,child,False,,Southampton,no,False 422 | 0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 423 | 0,3,male,21.0,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True 424 | 0,3,male,29.0,0,0,7.875,S,Third,man,True,,Southampton,no,True 425 | 0,3,female,28.0,1,1,14.4,S,Third,woman,False,,Southampton,no,False 426 | 0,3,male,18.0,1,1,20.2125,S,Third,man,True,,Southampton,no,False 427 | 0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True 428 | 1,2,female,28.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 429 | 1,2,female,19.0,0,0,26.0,S,Second,woman,False,,Southampton,yes,True 430 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 431 | 1,3,male,32.0,0,0,8.05,S,Third,man,True,E,Southampton,yes,True 432 | 1,1,male,28.0,0,0,26.55,S,First,man,True,C,Southampton,yes,True 433 | 1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False 434 | 1,2,female,42.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 435 | 0,3,male,17.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 436 | 0,1,male,50.0,1,0,55.9,S,First,man,True,E,Southampton,no,False 437 | 1,1,female,14.0,1,2,120.0,S,First,child,False,B,Southampton,yes,False 438 | 0,3,female,21.0,2,2,34.375,S,Third,woman,False,,Southampton,no,False 439 | 1,2,female,24.0,2,3,18.75,S,Second,woman,False,,Southampton,yes,False 440 | 0,1,male,64.0,1,4,263.0,S,First,man,True,C,Southampton,no,False 441 | 0,2,male,31.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 442 | 1,2,female,45.0,1,1,26.25,S,Second,woman,False,,Southampton,yes,False 443 | 0,3,male,20.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 444 | 0,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,no,False 445 | 1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 446 | 1,3,male,,0,0,8.1125,S,Third,man,True,,Southampton,yes,True 447 | 1,1,male,4.0,0,2,81.8583,S,First,child,False,A,Southampton,yes,False 448 | 1,2,female,13.0,0,1,19.5,S,Second,child,False,,Southampton,yes,False 449 | 1,1,male,34.0,0,0,26.55,S,First,man,True,,Southampton,yes,True 450 | 1,3,female,5.0,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False 451 | 1,1,male,52.0,0,0,30.5,S,First,man,True,C,Southampton,yes,True 452 | 0,2,male,36.0,1,2,27.75,S,Second,man,True,,Southampton,no,False 453 | 0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False 454 | 0,1,male,30.0,0,0,27.75,C,First,man,True,C,Cherbourg,no,True 455 | 1,1,male,49.0,1,0,89.1042,C,First,man,True,C,Cherbourg,yes,False 456 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 457 | 1,3,male,29.0,0,0,7.8958,C,Third,man,True,,Cherbourg,yes,True 458 | 0,1,male,65.0,0,0,26.55,S,First,man,True,E,Southampton,no,True 459 | 1,1,female,,1,0,51.8625,S,First,woman,False,D,Southampton,yes,False 460 | 1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 461 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 462 | 1,1,male,48.0,0,0,26.55,S,First,man,True,E,Southampton,yes,True 463 | 0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 464 | 0,1,male,47.0,0,0,38.5,S,First,man,True,E,Southampton,no,True 465 | 0,2,male,48.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 466 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 467 | 0,3,male,38.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 468 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 469 | 0,1,male,56.0,0,0,26.55,S,First,man,True,,Southampton,no,True 470 | 0,3,male,,0,0,7.725,Q,Third,man,True,,Queenstown,no,True 471 | 1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False 472 | 0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True 473 | 0,3,male,38.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 474 | 1,2,female,33.0,1,2,27.75,S,Second,woman,False,,Southampton,yes,False 475 | 1,2,female,23.0,0,0,13.7917,C,Second,woman,False,D,Cherbourg,yes,True 476 | 0,3,female,22.0,0,0,9.8375,S,Third,woman,False,,Southampton,no,True 477 | 0,1,male,,0,0,52.0,S,First,man,True,A,Southampton,no,True 478 | 0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False 479 | 0,3,male,29.0,1,0,7.0458,S,Third,man,True,,Southampton,no,False 480 | 0,3,male,22.0,0,0,7.5208,S,Third,man,True,,Southampton,no,True 481 | 1,3,female,2.0,0,1,12.2875,S,Third,child,False,,Southampton,yes,False 482 | 0,3,male,9.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 483 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 484 | 0,3,male,50.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 485 | 1,3,female,63.0,0,0,9.5875,S,Third,woman,False,,Southampton,yes,True 486 | 1,1,male,25.0,1,0,91.0792,C,First,man,True,B,Cherbourg,yes,False 487 | 0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False 488 | 1,1,female,35.0,1,0,90.0,S,First,woman,False,C,Southampton,yes,False 489 | 0,1,male,58.0,0,0,29.7,C,First,man,True,B,Cherbourg,no,True 490 | 0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 491 | 1,3,male,9.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False 492 | 0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False 493 | 0,3,male,21.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 494 | 0,1,male,55.0,0,0,30.5,S,First,man,True,C,Southampton,no,True 495 | 0,1,male,71.0,0,0,49.5042,C,First,man,True,,Cherbourg,no,True 496 | 0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 497 | 0,3,male,,0,0,14.4583,C,Third,man,True,,Cherbourg,no,True 498 | 1,1,female,54.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False 499 | 0,3,male,,0,0,15.1,S,Third,man,True,,Southampton,no,True 500 | 0,1,female,25.0,1,2,151.55,S,First,woman,False,C,Southampton,no,False 501 | 0,3,male,24.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 502 | 0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 503 | 0,3,female,21.0,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True 504 | 0,3,female,,0,0,7.6292,Q,Third,woman,False,,Queenstown,no,True 505 | 0,3,female,37.0,0,0,9.5875,S,Third,woman,False,,Southampton,no,True 506 | 1,1,female,16.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True 507 | 0,1,male,18.0,1,0,108.9,C,First,man,True,C,Cherbourg,no,False 508 | 1,2,female,33.0,0,2,26.0,S,Second,woman,False,,Southampton,yes,False 509 | 1,1,male,,0,0,26.55,S,First,man,True,,Southampton,yes,True 510 | 0,3,male,28.0,0,0,22.525,S,Third,man,True,,Southampton,no,True 511 | 1,3,male,26.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 512 | 1,3,male,29.0,0,0,7.75,Q,Third,man,True,,Queenstown,yes,True 513 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 514 | 1,1,male,36.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True 515 | 1,1,female,54.0,1,0,59.4,C,First,woman,False,,Cherbourg,yes,False 516 | 0,3,male,24.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True 517 | 0,1,male,47.0,0,0,34.0208,S,First,man,True,D,Southampton,no,True 518 | 1,2,female,34.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True 519 | 0,3,male,,0,0,24.15,Q,Third,man,True,,Queenstown,no,True 520 | 1,2,female,36.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 521 | 0,3,male,32.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 522 | 1,1,female,30.0,0,0,93.5,S,First,woman,False,B,Southampton,yes,True 523 | 0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 524 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 525 | 1,1,female,44.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False 526 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 527 | 0,3,male,40.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 528 | 1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 529 | 0,1,male,,0,0,221.7792,S,First,man,True,C,Southampton,no,True 530 | 0,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 531 | 0,2,male,23.0,2,1,11.5,S,Second,man,True,,Southampton,no,False 532 | 1,2,female,2.0,1,1,26.0,S,Second,child,False,,Southampton,yes,False 533 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 534 | 0,3,male,17.0,1,1,7.2292,C,Third,man,True,,Cherbourg,no,False 535 | 1,3,female,,0,2,22.3583,C,Third,woman,False,,Cherbourg,yes,False 536 | 0,3,female,30.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True 537 | 1,2,female,7.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False 538 | 0,1,male,45.0,0,0,26.55,S,First,man,True,B,Southampton,no,True 539 | 1,1,female,30.0,0,0,106.425,C,First,woman,False,,Cherbourg,yes,True 540 | 0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True 541 | 1,1,female,22.0,0,2,49.5,C,First,woman,False,B,Cherbourg,yes,False 542 | 1,1,female,36.0,0,2,71.0,S,First,woman,False,B,Southampton,yes,False 543 | 0,3,female,9.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 544 | 0,3,female,11.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 545 | 1,2,male,32.0,1,0,26.0,S,Second,man,True,,Southampton,yes,False 546 | 0,1,male,50.0,1,0,106.425,C,First,man,True,C,Cherbourg,no,False 547 | 0,1,male,64.0,0,0,26.0,S,First,man,True,,Southampton,no,True 548 | 1,2,female,19.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 549 | 1,2,male,,0,0,13.8625,C,Second,man,True,,Cherbourg,yes,True 550 | 0,3,male,33.0,1,1,20.525,S,Third,man,True,,Southampton,no,False 551 | 1,2,male,8.0,1,1,36.75,S,Second,child,False,,Southampton,yes,False 552 | 1,1,male,17.0,0,2,110.8833,C,First,man,True,C,Cherbourg,yes,False 553 | 0,2,male,27.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 554 | 0,3,male,,0,0,7.8292,Q,Third,man,True,,Queenstown,no,True 555 | 1,3,male,22.0,0,0,7.225,C,Third,man,True,,Cherbourg,yes,True 556 | 1,3,female,22.0,0,0,7.775,S,Third,woman,False,,Southampton,yes,True 557 | 0,1,male,62.0,0,0,26.55,S,First,man,True,,Southampton,no,True 558 | 1,1,female,48.0,1,0,39.6,C,First,woman,False,A,Cherbourg,yes,False 559 | 0,1,male,,0,0,227.525,C,First,man,True,,Cherbourg,no,True 560 | 1,1,female,39.0,1,1,79.65,S,First,woman,False,E,Southampton,yes,False 561 | 1,3,female,36.0,1,0,17.4,S,Third,woman,False,,Southampton,yes,False 562 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 563 | 0,3,male,40.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 564 | 0,2,male,28.0,0,0,13.5,S,Second,man,True,,Southampton,no,True 565 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 566 | 0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True 567 | 0,3,male,24.0,2,0,24.15,S,Third,man,True,,Southampton,no,False 568 | 0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 569 | 0,3,female,29.0,0,4,21.075,S,Third,woman,False,,Southampton,no,False 570 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 571 | 1,3,male,32.0,0,0,7.8542,S,Third,man,True,,Southampton,yes,True 572 | 1,2,male,62.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True 573 | 1,1,female,53.0,2,0,51.4792,S,First,woman,False,C,Southampton,yes,False 574 | 1,1,male,36.0,0,0,26.3875,S,First,man,True,E,Southampton,yes,True 575 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 576 | 0,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 577 | 0,3,male,19.0,0,0,14.5,S,Third,man,True,,Southampton,no,True 578 | 1,2,female,34.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 579 | 1,1,female,39.0,1,0,55.9,S,First,woman,False,E,Southampton,yes,False 580 | 0,3,female,,1,0,14.4583,C,Third,woman,False,,Cherbourg,no,False 581 | 1,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 582 | 1,2,female,25.0,1,1,30.0,S,Second,woman,False,,Southampton,yes,False 583 | 1,1,female,39.0,1,1,110.8833,C,First,woman,False,C,Cherbourg,yes,False 584 | 0,2,male,54.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 585 | 0,1,male,36.0,0,0,40.125,C,First,man,True,A,Cherbourg,no,True 586 | 0,3,male,,0,0,8.7125,C,Third,man,True,,Cherbourg,no,True 587 | 1,1,female,18.0,0,2,79.65,S,First,woman,False,E,Southampton,yes,False 588 | 0,2,male,47.0,0,0,15.0,S,Second,man,True,,Southampton,no,True 589 | 1,1,male,60.0,1,1,79.2,C,First,man,True,B,Cherbourg,yes,False 590 | 0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 591 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 592 | 0,3,male,35.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 593 | 1,1,female,52.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False 594 | 0,3,male,47.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 595 | 0,3,female,,0,2,7.75,Q,Third,woman,False,,Queenstown,no,False 596 | 0,2,male,37.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 597 | 0,3,male,36.0,1,1,24.15,S,Third,man,True,,Southampton,no,False 598 | 1,2,female,,0,0,33.0,S,Second,woman,False,,Southampton,yes,True 599 | 0,3,male,49.0,0,0,0.0,S,Third,man,True,,Southampton,no,True 600 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 601 | 1,1,male,49.0,1,0,56.9292,C,First,man,True,A,Cherbourg,yes,False 602 | 1,2,female,24.0,2,1,27.0,S,Second,woman,False,,Southampton,yes,False 603 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 604 | 0,1,male,,0,0,42.4,S,First,man,True,,Southampton,no,True 605 | 0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 606 | 1,1,male,35.0,0,0,26.55,C,First,man,True,,Cherbourg,yes,True 607 | 0,3,male,36.0,1,0,15.55,S,Third,man,True,,Southampton,no,False 608 | 0,3,male,30.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 609 | 1,1,male,27.0,0,0,30.5,S,First,man,True,,Southampton,yes,True 610 | 1,2,female,22.0,1,2,41.5792,C,Second,woman,False,,Cherbourg,yes,False 611 | 1,1,female,40.0,0,0,153.4625,S,First,woman,False,C,Southampton,yes,True 612 | 0,3,female,39.0,1,5,31.275,S,Third,woman,False,,Southampton,no,False 613 | 0,3,male,,0,0,7.05,S,Third,man,True,,Southampton,no,True 614 | 1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False 615 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 616 | 0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 617 | 1,2,female,24.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False 618 | 0,3,male,34.0,1,1,14.4,S,Third,man,True,,Southampton,no,False 619 | 0,3,female,26.0,1,0,16.1,S,Third,woman,False,,Southampton,no,False 620 | 1,2,female,4.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False 621 | 0,2,male,26.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 622 | 0,3,male,27.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False 623 | 1,1,male,42.0,1,0,52.5542,S,First,man,True,D,Southampton,yes,False 624 | 1,3,male,20.0,1,1,15.7417,C,Third,man,True,,Cherbourg,yes,False 625 | 0,3,male,21.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 626 | 0,3,male,21.0,0,0,16.1,S,Third,man,True,,Southampton,no,True 627 | 0,1,male,61.0,0,0,32.3208,S,First,man,True,D,Southampton,no,True 628 | 0,2,male,57.0,0,0,12.35,Q,Second,man,True,,Queenstown,no,True 629 | 1,1,female,21.0,0,0,77.9583,S,First,woman,False,D,Southampton,yes,True 630 | 0,3,male,26.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 631 | 0,3,male,,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True 632 | 1,1,male,80.0,0,0,30.0,S,First,man,True,A,Southampton,yes,True 633 | 0,3,male,51.0,0,0,7.0542,S,Third,man,True,,Southampton,no,True 634 | 1,1,male,32.0,0,0,30.5,C,First,man,True,B,Cherbourg,yes,True 635 | 0,1,male,,0,0,0.0,S,First,man,True,,Southampton,no,True 636 | 0,3,female,9.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 637 | 1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 638 | 0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 639 | 0,2,male,31.0,1,1,26.25,S,Second,man,True,,Southampton,no,False 640 | 0,3,female,41.0,0,5,39.6875,S,Third,woman,False,,Southampton,no,False 641 | 0,3,male,,1,0,16.1,S,Third,man,True,,Southampton,no,False 642 | 0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 643 | 1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True 644 | 0,3,female,2.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 645 | 1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 646 | 1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False 647 | 1,1,male,48.0,1,0,76.7292,C,First,man,True,D,Cherbourg,yes,False 648 | 0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 649 | 1,1,male,56.0,0,0,35.5,C,First,man,True,A,Cherbourg,yes,True 650 | 0,3,male,,0,0,7.55,S,Third,man,True,,Southampton,no,True 651 | 1,3,female,23.0,0,0,7.55,S,Third,woman,False,,Southampton,yes,True 652 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 653 | 1,2,female,18.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False 654 | 0,3,male,21.0,0,0,8.4333,S,Third,man,True,,Southampton,no,True 655 | 1,3,female,,0,0,7.8292,Q,Third,woman,False,,Queenstown,yes,True 656 | 0,3,female,18.0,0,0,6.75,Q,Third,woman,False,,Queenstown,no,True 657 | 0,2,male,24.0,2,0,73.5,S,Second,man,True,,Southampton,no,False 658 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 659 | 0,3,female,32.0,1,1,15.5,Q,Third,woman,False,,Queenstown,no,False 660 | 0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 661 | 0,1,male,58.0,0,2,113.275,C,First,man,True,D,Cherbourg,no,False 662 | 1,1,male,50.0,2,0,133.65,S,First,man,True,,Southampton,yes,False 663 | 0,3,male,40.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 664 | 0,1,male,47.0,0,0,25.5875,S,First,man,True,E,Southampton,no,True 665 | 0,3,male,36.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True 666 | 1,3,male,20.0,1,0,7.925,S,Third,man,True,,Southampton,yes,False 667 | 0,2,male,32.0,2,0,73.5,S,Second,man,True,,Southampton,no,False 668 | 0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 669 | 0,3,male,,0,0,7.775,S,Third,man,True,,Southampton,no,True 670 | 0,3,male,43.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 671 | 1,1,female,,1,0,52.0,S,First,woman,False,C,Southampton,yes,False 672 | 1,2,female,40.0,1,1,39.0,S,Second,woman,False,,Southampton,yes,False 673 | 0,1,male,31.0,1,0,52.0,S,First,man,True,B,Southampton,no,False 674 | 0,2,male,70.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 675 | 1,2,male,31.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True 676 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 677 | 0,3,male,18.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 678 | 0,3,male,24.5,0,0,8.05,S,Third,man,True,,Southampton,no,True 679 | 1,3,female,18.0,0,0,9.8417,S,Third,woman,False,,Southampton,yes,True 680 | 0,3,female,43.0,1,6,46.9,S,Third,woman,False,,Southampton,no,False 681 | 1,1,male,36.0,0,1,512.3292,C,First,man,True,B,Cherbourg,yes,False 682 | 0,3,female,,0,0,8.1375,Q,Third,woman,False,,Queenstown,no,True 683 | 1,1,male,27.0,0,0,76.7292,C,First,man,True,D,Cherbourg,yes,True 684 | 0,3,male,20.0,0,0,9.225,S,Third,man,True,,Southampton,no,True 685 | 0,3,male,14.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 686 | 0,2,male,60.0,1,1,39.0,S,Second,man,True,,Southampton,no,False 687 | 0,2,male,25.0,1,2,41.5792,C,Second,man,True,,Cherbourg,no,False 688 | 0,3,male,14.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 689 | 0,3,male,19.0,0,0,10.1708,S,Third,man,True,,Southampton,no,True 690 | 0,3,male,18.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 691 | 1,1,female,15.0,0,1,211.3375,S,First,child,False,B,Southampton,yes,False 692 | 1,1,male,31.0,1,0,57.0,S,First,man,True,B,Southampton,yes,False 693 | 1,3,female,4.0,0,1,13.4167,C,Third,child,False,,Cherbourg,yes,False 694 | 1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 695 | 0,3,male,25.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 696 | 0,1,male,60.0,0,0,26.55,S,First,man,True,,Southampton,no,True 697 | 0,2,male,52.0,0,0,13.5,S,Second,man,True,,Southampton,no,True 698 | 0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 699 | 1,3,female,,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True 700 | 0,1,male,49.0,1,1,110.8833,C,First,man,True,C,Cherbourg,no,False 701 | 0,3,male,42.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True 702 | 1,1,female,18.0,1,0,227.525,C,First,woman,False,C,Cherbourg,yes,False 703 | 1,1,male,35.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True 704 | 0,3,female,18.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False 705 | 0,3,male,25.0,0,0,7.7417,Q,Third,man,True,,Queenstown,no,True 706 | 0,3,male,26.0,1,0,7.8542,S,Third,man,True,,Southampton,no,False 707 | 0,2,male,39.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 708 | 1,2,female,45.0,0,0,13.5,S,Second,woman,False,,Southampton,yes,True 709 | 1,1,male,42.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True 710 | 1,1,female,22.0,0,0,151.55,S,First,woman,False,,Southampton,yes,True 711 | 1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False 712 | 1,1,female,24.0,0,0,49.5042,C,First,woman,False,C,Cherbourg,yes,True 713 | 0,1,male,,0,0,26.55,S,First,man,True,C,Southampton,no,True 714 | 1,1,male,48.0,1,0,52.0,S,First,man,True,C,Southampton,yes,False 715 | 0,3,male,29.0,0,0,9.4833,S,Third,man,True,,Southampton,no,True 716 | 0,2,male,52.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 717 | 0,3,male,19.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True 718 | 1,1,female,38.0,0,0,227.525,C,First,woman,False,C,Cherbourg,yes,True 719 | 1,2,female,27.0,0,0,10.5,S,Second,woman,False,E,Southampton,yes,True 720 | 0,3,male,,0,0,15.5,Q,Third,man,True,,Queenstown,no,True 721 | 0,3,male,33.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 722 | 1,2,female,6.0,0,1,33.0,S,Second,child,False,,Southampton,yes,False 723 | 0,3,male,17.0,1,0,7.0542,S,Third,man,True,,Southampton,no,False 724 | 0,2,male,34.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 725 | 0,2,male,50.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 726 | 1,1,male,27.0,1,0,53.1,S,First,man,True,E,Southampton,yes,False 727 | 0,3,male,20.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 728 | 1,2,female,30.0,3,0,21.0,S,Second,woman,False,,Southampton,yes,False 729 | 1,3,female,,0,0,7.7375,Q,Third,woman,False,,Queenstown,yes,True 730 | 0,2,male,25.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 731 | 0,3,female,25.0,1,0,7.925,S,Third,woman,False,,Southampton,no,False 732 | 1,1,female,29.0,0,0,211.3375,S,First,woman,False,B,Southampton,yes,True 733 | 0,3,male,11.0,0,0,18.7875,C,Third,child,False,,Cherbourg,no,True 734 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 735 | 0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 736 | 0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 737 | 0,3,male,28.5,0,0,16.1,S,Third,man,True,,Southampton,no,True 738 | 0,3,female,48.0,1,3,34.375,S,Third,woman,False,,Southampton,no,False 739 | 1,1,male,35.0,0,0,512.3292,C,First,man,True,B,Cherbourg,yes,True 740 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 741 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 742 | 1,1,male,,0,0,30.0,S,First,man,True,D,Southampton,yes,True 743 | 0,1,male,36.0,1,0,78.85,S,First,man,True,C,Southampton,no,False 744 | 1,1,female,21.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False 745 | 0,3,male,24.0,1,0,16.1,S,Third,man,True,,Southampton,no,False 746 | 1,3,male,31.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 747 | 0,1,male,70.0,1,1,71.0,S,First,man,True,B,Southampton,no,False 748 | 0,3,male,16.0,1,1,20.25,S,Third,man,True,,Southampton,no,False 749 | 1,2,female,30.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 750 | 0,1,male,19.0,1,0,53.1,S,First,man,True,D,Southampton,no,False 751 | 0,3,male,31.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 752 | 1,2,female,4.0,1,1,23.0,S,Second,child,False,,Southampton,yes,False 753 | 1,3,male,6.0,0,1,12.475,S,Third,child,False,E,Southampton,yes,False 754 | 0,3,male,33.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 755 | 0,3,male,23.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 756 | 1,2,female,48.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False 757 | 1,2,male,0.67,1,1,14.5,S,Second,child,False,,Southampton,yes,False 758 | 0,3,male,28.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 759 | 0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True 760 | 0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 761 | 1,1,female,33.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True 762 | 0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True 763 | 0,3,male,41.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 764 | 1,3,male,20.0,0,0,7.2292,C,Third,man,True,,Cherbourg,yes,True 765 | 1,1,female,36.0,1,2,120.0,S,First,woman,False,B,Southampton,yes,False 766 | 0,3,male,16.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 767 | 1,1,female,51.0,1,0,77.9583,S,First,woman,False,D,Southampton,yes,False 768 | 0,1,male,,0,0,39.6,C,First,man,True,,Cherbourg,no,True 769 | 0,3,female,30.5,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True 770 | 0,3,male,,1,0,24.15,Q,Third,man,True,,Queenstown,no,False 771 | 0,3,male,32.0,0,0,8.3625,S,Third,man,True,,Southampton,no,True 772 | 0,3,male,24.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 773 | 0,3,male,48.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 774 | 0,2,female,57.0,0,0,10.5,S,Second,woman,False,E,Southampton,no,True 775 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 776 | 1,2,female,54.0,1,3,23.0,S,Second,woman,False,,Southampton,yes,False 777 | 0,3,male,18.0,0,0,7.75,S,Third,man,True,,Southampton,no,True 778 | 0,3,male,,0,0,7.75,Q,Third,man,True,F,Queenstown,no,True 779 | 1,3,female,5.0,0,0,12.475,S,Third,child,False,,Southampton,yes,True 780 | 0,3,male,,0,0,7.7375,Q,Third,man,True,,Queenstown,no,True 781 | 1,1,female,43.0,0,1,211.3375,S,First,woman,False,B,Southampton,yes,False 782 | 1,3,female,13.0,0,0,7.2292,C,Third,child,False,,Cherbourg,yes,True 783 | 1,1,female,17.0,1,0,57.0,S,First,woman,False,B,Southampton,yes,False 784 | 0,1,male,29.0,0,0,30.0,S,First,man,True,D,Southampton,no,True 785 | 0,3,male,,1,2,23.45,S,Third,man,True,,Southampton,no,False 786 | 0,3,male,25.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 787 | 0,3,male,25.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 788 | 1,3,female,18.0,0,0,7.4958,S,Third,woman,False,,Southampton,yes,True 789 | 0,3,male,8.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 790 | 1,3,male,1.0,1,2,20.575,S,Third,child,False,,Southampton,yes,False 791 | 0,1,male,46.0,0,0,79.2,C,First,man,True,B,Cherbourg,no,True 792 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 793 | 0,2,male,16.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 794 | 0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False 795 | 0,1,male,,0,0,30.6958,C,First,man,True,,Cherbourg,no,True 796 | 0,3,male,25.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 797 | 0,2,male,39.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 798 | 1,1,female,49.0,0,0,25.9292,S,First,woman,False,D,Southampton,yes,True 799 | 1,3,female,31.0,0,0,8.6833,S,Third,woman,False,,Southampton,yes,True 800 | 0,3,male,30.0,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 801 | 0,3,female,30.0,1,1,24.15,S,Third,woman,False,,Southampton,no,False 802 | 0,2,male,34.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 803 | 1,2,female,31.0,1,1,26.25,S,Second,woman,False,,Southampton,yes,False 804 | 1,1,male,11.0,1,2,120.0,S,First,child,False,B,Southampton,yes,False 805 | 1,3,male,0.42,0,1,8.5167,C,Third,child,False,,Cherbourg,yes,False 806 | 1,3,male,27.0,0,0,6.975,S,Third,man,True,,Southampton,yes,True 807 | 0,3,male,31.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 808 | 0,1,male,39.0,0,0,0.0,S,First,man,True,A,Southampton,no,True 809 | 0,3,female,18.0,0,0,7.775,S,Third,woman,False,,Southampton,no,True 810 | 0,2,male,39.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 811 | 1,1,female,33.0,1,0,53.1,S,First,woman,False,E,Southampton,yes,False 812 | 0,3,male,26.0,0,0,7.8875,S,Third,man,True,,Southampton,no,True 813 | 0,3,male,39.0,0,0,24.15,S,Third,man,True,,Southampton,no,True 814 | 0,2,male,35.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 815 | 0,3,female,6.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 816 | 0,3,male,30.5,0,0,8.05,S,Third,man,True,,Southampton,no,True 817 | 0,1,male,,0,0,0.0,S,First,man,True,B,Southampton,no,True 818 | 0,3,female,23.0,0,0,7.925,S,Third,woman,False,,Southampton,no,True 819 | 0,2,male,31.0,1,1,37.0042,C,Second,man,True,,Cherbourg,no,False 820 | 0,3,male,43.0,0,0,6.45,S,Third,man,True,,Southampton,no,True 821 | 0,3,male,10.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 822 | 1,1,female,52.0,1,1,93.5,S,First,woman,False,B,Southampton,yes,False 823 | 1,3,male,27.0,0,0,8.6625,S,Third,man,True,,Southampton,yes,True 824 | 0,1,male,38.0,0,0,0.0,S,First,man,True,,Southampton,no,True 825 | 1,3,female,27.0,0,1,12.475,S,Third,woman,False,E,Southampton,yes,False 826 | 0,3,male,2.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 827 | 0,3,male,,0,0,6.95,Q,Third,man,True,,Queenstown,no,True 828 | 0,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,no,True 829 | 1,2,male,1.0,0,2,37.0042,C,Second,child,False,,Cherbourg,yes,False 830 | 1,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,yes,True 831 | 1,1,female,62.0,0,0,80.0,,First,woman,False,B,,yes,True 832 | 1,3,female,15.0,1,0,14.4542,C,Third,child,False,,Cherbourg,yes,False 833 | 1,2,male,0.83,1,1,18.75,S,Second,child,False,,Southampton,yes,False 834 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 835 | 0,3,male,23.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 836 | 0,3,male,18.0,0,0,8.3,S,Third,man,True,,Southampton,no,True 837 | 1,1,female,39.0,1,1,83.1583,C,First,woman,False,E,Cherbourg,yes,False 838 | 0,3,male,21.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 839 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 840 | 1,3,male,32.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 841 | 1,1,male,,0,0,29.7,C,First,man,True,C,Cherbourg,yes,True 842 | 0,3,male,20.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 843 | 0,2,male,16.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 844 | 1,1,female,30.0,0,0,31.0,C,First,woman,False,,Cherbourg,yes,True 845 | 0,3,male,34.5,0,0,6.4375,C,Third,man,True,,Cherbourg,no,True 846 | 0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 847 | 0,3,male,42.0,0,0,7.55,S,Third,man,True,,Southampton,no,True 848 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 849 | 0,3,male,35.0,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 850 | 0,2,male,28.0,0,1,33.0,S,Second,man,True,,Southampton,no,False 851 | 1,1,female,,1,0,89.1042,C,First,woman,False,C,Cherbourg,yes,False 852 | 0,3,male,4.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 853 | 0,3,male,74.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 854 | 0,3,female,9.0,1,1,15.2458,C,Third,child,False,,Cherbourg,no,False 855 | 1,1,female,16.0,0,1,39.4,S,First,woman,False,D,Southampton,yes,False 856 | 0,2,female,44.0,1,0,26.0,S,Second,woman,False,,Southampton,no,False 857 | 1,3,female,18.0,0,1,9.35,S,Third,woman,False,,Southampton,yes,False 858 | 1,1,female,45.0,1,1,164.8667,S,First,woman,False,,Southampton,yes,False 859 | 1,1,male,51.0,0,0,26.55,S,First,man,True,E,Southampton,yes,True 860 | 1,3,female,24.0,0,3,19.2583,C,Third,woman,False,,Cherbourg,yes,False 861 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 862 | 0,3,male,41.0,2,0,14.1083,S,Third,man,True,,Southampton,no,False 863 | 0,2,male,21.0,1,0,11.5,S,Second,man,True,,Southampton,no,False 864 | 1,1,female,48.0,0,0,25.9292,S,First,woman,False,D,Southampton,yes,True 865 | 0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False 866 | 0,2,male,24.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 867 | 1,2,female,42.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 868 | 1,2,female,27.0,1,0,13.8583,C,Second,woman,False,,Cherbourg,yes,False 869 | 0,1,male,31.0,0,0,50.4958,S,First,man,True,A,Southampton,no,True 870 | 0,3,male,,0,0,9.5,S,Third,man,True,,Southampton,no,True 871 | 1,3,male,4.0,1,1,11.1333,S,Third,child,False,,Southampton,yes,False 872 | 0,3,male,26.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 873 | 1,1,female,47.0,1,1,52.5542,S,First,woman,False,D,Southampton,yes,False 874 | 0,1,male,33.0,0,0,5.0,S,First,man,True,B,Southampton,no,True 875 | 0,3,male,47.0,0,0,9.0,S,Third,man,True,,Southampton,no,True 876 | 1,2,female,28.0,1,0,24.0,C,Second,woman,False,,Cherbourg,yes,False 877 | 1,3,female,15.0,0,0,7.225,C,Third,child,False,,Cherbourg,yes,True 878 | 0,3,male,20.0,0,0,9.8458,S,Third,man,True,,Southampton,no,True 879 | 0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 880 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 881 | 1,1,female,56.0,0,1,83.1583,C,First,woman,False,C,Cherbourg,yes,False 882 | 1,2,female,25.0,0,1,26.0,S,Second,woman,False,,Southampton,yes,False 883 | 0,3,male,33.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 884 | 0,3,female,22.0,0,0,10.5167,S,Third,woman,False,,Southampton,no,True 885 | 0,2,male,28.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 886 | 0,3,male,25.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 887 | 0,3,female,39.0,0,5,29.125,Q,Third,woman,False,,Queenstown,no,False 888 | 0,2,male,27.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 889 | 1,1,female,19.0,0,0,30.0,S,First,woman,False,B,Southampton,yes,True 890 | 0,3,female,,1,2,23.45,S,Third,woman,False,,Southampton,no,False 891 | 1,1,male,26.0,0,0,30.0,C,First,man,True,C,Cherbourg,yes,True 892 | 0,3,male,32.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 893 | --------------------------------------------------------------------------------