<prompt> Theha Jupyter Notebook ho hlahloba datha.housing_data.csv  <response>: Instruction in English: Create a Jupyter Notebook to explore a dataset. . Response in English: Here is a Jupyter Notebook that you can use to explore the housing_data.csv dataset. Step 1: Set up the Jupyter NotebookFirst, you will need to install and set up Jupyter Notebook on your local machine. You can find detailed instructions on how to do this on the projects official webpage:Once you have set up the Jupyter Notebook, open it and create a new notebook. Step 2: Import necessary librariesBefore we start exploring the dataset, we need to import the necessary libraries. You will be using pandas, matplotlib, and seaborn to analyze and visualize the data. Make sure to install these libraries using !pip install library_name if you havent done so already. Then, in the first cell of your Jupyter Notebook, enter the following code:pythonimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns Step 3: Load the datasetIn the next cell, enter the following code to load the dataset into a pandas DataFrame:pythondf pd.read_csvhousing_data.csvMake sure that the file housing_data.csv is in the same directory as your Jupyter Notebook. Step 4: Explore the datasetNow that the dataset is loaded into a DataFrame, you can start exploring it. There are several ways to do so. Here are some suggested steps:1. View the first few rows of the DataFrame using the head method: python df.head 2. Check the shape of the DataFrame to see how many rows and columns there are: python df.shape 3. Get a summary of the DataFrame using the info method: python df.info 4. Use the describe method to generate summary statistics of the numerical columns: python df.describe 5. Check for missing values: python df.isnull.sum Step 5: Visualize the dataYou can also use visualization techniques to explore the data further. Here are some suggested visualizations:1. Use a histogram to see the distribution of a numerical column: python sns.histplotdfcolumn_name plt.show 2. Use a boxplot . Response in Sesotho: Buka ea Jupyter Notebook ke ena eo u ka e sebelisang ho hlahloba dataset ea housing_data.csv. Mohato oa 1: Theha Jupyter Notebook Pele, o tla hloka ho kenya le ho theha Jupyter Notebook mochining oa heno. U ka fumana litaelo tse qaqileng mabapi le mokhoa oa ho etsa sena leqepheng la semmuso la projeke: Hang ha u se u thehile Jupyter Notebook, e bule me u thehe bukana e ncha. Mohato oa 2: Kenya lilaeborari tse hlokahalang Pele re qala ho batlisisa datha, re hloka ho kenya lilaeborari tse hlokahalang. U tla be u sebelisa li-pandas, matplotlib, le seaborn ho sekaseka le ho bona lintlha ka mahlo a kelello. Netefatsa hore o kenya lilaebrari tsena ka !pip install library_name haeba ha u so etse joalo. Ebe, ka seleng ea pele ea Jupyter Notebook, kenya khoutu e latelang: python import pandas as pd import matplotlib.pyplot as plt import seaborn as sns Mohato 3: Laola dataset Seleng e latelang, kenya khoutu e latelang ea ho kenya dataset ho pandas DataFrame: python df pd.read_csvhousing_data.csv Etsa bonnete ba hore faele ea housing_data.csv e bukeng e tšoanang le Jupyter Notebook ea hau. . Mohato oa 4: Lekola datha Kaha joale dataset e kentsoe ho DataFrame, u ka qala ho e hlahloba. Ho na le litsela tse maloa tsa ho etsa joalo. Mehato e meng e sisintsweng ke ena: 1. Sheba mela e mmalwa ya pele ya DataFrame o sebedisa mokgwa wa hlooho: python df.head 2. Sheba sebopeho sa DataFrame ho bona hore na mela e mekae. me likholomo li teng: python df.shape 3. Fumana kakaretso ea DataFrame u sebelisa info mokhoa: python df.info 4. Sebelisa tlhaloso mokhoa oa ho hlahisa kakaretso ea lipalo-palo tsa likholomo tsa linomoro: python df.describe 5. Hlahloba boleng bo sieo: python df.isnull.sum Mohato 5: Bona lintlha ka mahlo a kelello U ka boela ua sebelisa mekhoa ea pono ho hlahloba lintlha ho ea pele. Litlhahiso tse khothaletsoang ke tsena: 1. Sebelisa histogram ho bona kabo ea kholomo ea linomoro: python sns.histplotdfcolumn_name plt.show 2. Sebelisa lebokose la lebokose 