{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":84896,"databundleVersionId":10305135,"sourceType":"competition"}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Kaggle Tabular Playground Series Season 4 Eps 12: The data Exploration","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport polars as pl\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-02T01:50:07.534253Z","iopub.execute_input":"2024-12-02T01:50:07.534826Z","iopub.status.idle":"2024-12-02T01:50:09.196664Z","shell.execute_reply.started":"2024-12-02T01:50:07.534737Z","shell.execute_reply":"2024-12-02T01:50:09.195376Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df = pl.read_csv('/kaggle/input/playground-series-s4e12/train.csv')\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T01:50:15.064437Z","iopub.execute_input":"2024-12-02T01:50:15.065009Z","iopub.status.idle":"2024-12-02T01:50:16.206072Z","shell.execute_reply.started":"2024-12-02T01:50:15.064969Z","shell.execute_reply":"2024-12-02T01:50:16.204865Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Here is a brief explanation of what each variable represents:\n\n1. Age: Numeric; the age of the insurance holder \n2. Gender: Categorical; the gender of the insurance holder\n3. Annual Income: The income of the insurance holder per year. There is no information on the currency unit\n4. Marital Status: Categorical; the marital status of the insurance holder\n5. Number of Dependents: Numeric; the total people the insurance holder needs to support financially.\n6. Education level: Categorical; the highest degree the insurance holder obtain.\n7. Occupation: Categorical; the employment status of the insurance holder.\n8. Health Score: Numeric; the score represents how healthy the insurance holder is.\n9. Location: Categorical; the environmental type where the insurance holder lives.\n10. Policy Type: Categorical; the insurance type each person has.\n11. Previous Claims: Categorical (Numeric); the total claims previously done.\n12. Vehicle Age: Numeric; the age of the insurance holder's vehicle.\n13. Credit Score: Numeric; the insurance holder's credit score.\n14. Insurance Duration: Numeric; the total years an insurance holder has had the insurance.\n15. Policy Start Date: Datetime; the starting date (day 1) when an insurance holder first buy the insurance.\n16. Customer Feedback: Categorical; the insurance holder's satisfaction.\n17. Smoking Status: Categorical; whether the insurance holder is smoking.\n18. Exercise Frequency: Categorical; how often the insurance holder does exercise.\n19. Property Type: Categorical; the type of the property the insurance holder has.\n20. Premium Amount: Numeric; the amount of money the insurance holder needs to pay (usually annually, even though there is no explicit information on this dataset). This is the target feature.\n\n The table below shows the total missing values for each column in this dataset.","metadata":{}},{"cell_type":"code","source":"print(f\"The shape of the dataframe: {df.shape}\")\ndf.null_count()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T01:53:00.776578Z","iopub.execute_input":"2024-12-02T01:53:00.777391Z","iopub.status.idle":"2024-12-02T01:53:00.786196Z","shell.execute_reply.started":"2024-12-02T01:53:00.777345Z","shell.execute_reply":"2024-12-02T01:53:00.784999Z"},"_kg_hide-input":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":" If we consider using only the data without missing values, than we would have only a third of the original data's size..","metadata":{}},{"cell_type":"code","source":"df.drop_nulls().shape #drop any row missing any data, even that subject has only 1 missing value","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T01:54:53.527468Z","iopub.execute_input":"2024-12-02T01:54:53.527934Z","iopub.status.idle":"2024-12-02T01:54:53.658459Z","shell.execute_reply.started":"2024-12-02T01:54:53.527896Z","shell.execute_reply":"2024-12-02T01:54:53.657352Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# we will fill the NAs later\ndf_filled = pl.read_csv('/kaggle/input/playground-series-s4e12/train.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-01T17:00:48.65252Z","iopub.execute_input":"2024-12-01T17:00:48.653375Z","iopub.status.idle":"2024-12-01T17:00:49.355553Z","shell.execute_reply.started":"2024-12-01T17:00:48.653335Z","shell.execute_reply":"2024-12-01T17:00:49.354361Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Numerical Values\nLet's begin exploring the dataset. I'll start with the numerical data, and then the categorical ones.","metadata":{},"attachments":{}},{"cell_type":"markdown","source":"## First, the target π―\nThe following is the histogram of `Premium Amount`. From this histogram, we can see it has multimodal, indicating that **there might be a mixture of distributions.** The first mode is around 100 and it has the highest frequency. The second mode is around 700, and the third mode is a bit higher than 2000. As the mode goes higher, the frequency goes lower.","metadata":{}},{"cell_type":"code","source":"sns.histplot(df['Premium Amount'], bins=100, color='#377ec8');","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:06:08.736403Z","iopub.execute_input":"2024-12-02T02:06:08.736846Z","iopub.status.idle":"2024-12-02T02:06:10.118823Z","shell.execute_reply.started":"2024-12-02T02:06:08.736805Z","shell.execute_reply":"2024-12-02T02:06:10.117406Z"},"_kg_hide-input":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Age π\n The age ranges from 18 to 64, and it has roughly uniform distribution.","metadata":{},"attachments":{}},{"cell_type":"code","source":"df['Age'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:09:26.243219Z","iopub.execute_input":"2024-12-02T02:09:26.243856Z","iopub.status.idle":"2024-12-02T02:09:26.325816Z","shell.execute_reply.started":"2024-12-02T02:09:26.243757Z","shell.execute_reply":"2024-12-02T02:09:26.323869Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"_, axs = plt.subplots(1, 2, figsize=(12,6))\nsns.histplot(df['Age'], discrete=True, color='#377ec8', ax=axs[0])\nsns.boxplot(data=df['Age'], color='#7EC837', ax=axs[1]);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:10:36.204786Z","iopub.execute_input":"2024-12-02T02:10:36.205191Z","iopub.status.idle":"2024-12-02T02:10:37.642364Z","shell.execute_reply.started":"2024-12-02T02:10:36.205156Z","shell.execute_reply":"2024-12-02T02:10:37.641252Z"},"_kg_hide-input":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Annual Income π°\n As we can see from the histogram, this feature has a heavily right-skewed distribution. The histogram excludes the 3% missing data.","metadata":{}},{"cell_type":"code","source":"df['Annual Income'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:14:19.306568Z","iopub.execute_input":"2024-12-02T02:14:19.307028Z","iopub.status.idle":"2024-12-02T02:14:19.369845Z","shell.execute_reply.started":"2024-12-02T02:14:19.306987Z","shell.execute_reply":"2024-12-02T02:14:19.368313Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"_, axs = plt.subplots(1, 2, figsize=(12,6))\nsns.histplot(df['Annual Income'], bins=100, color='#377ec8', ax=axs[0])\nsns.boxplot(data=df['Annual Income'], color='#7ec837', ax=axs[1]);","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-01T17:12:46.038077Z","iopub.execute_input":"2024-12-01T17:12:46.038494Z","iopub.status.idle":"2024-12-01T17:12:47.734641Z","shell.execute_reply.started":"2024-12-01T17:12:46.038457Z","shell.execute_reply":"2024-12-01T17:12:47.733633Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Number of Dependents πΆπ΄π΅\n","metadata":{},"attachments":{}},{"cell_type":"code","source":"df['Number of Dependents'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:29:09.224588Z","iopub.execute_input":"2024-12-02T02:29:09.225906Z","iopub.status.idle":"2024-12-02T02:29:09.279084Z","shell.execute_reply.started":"2024-12-02T02:29:09.225841Z","shell.execute_reply":"2024-12-02T02:29:09.277715Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"_, axs = plt.subplots(1, 2, figsize=(12,6))\nsns.histplot(df['Number of Dependents'], discrete=True, color='#377ec8', ax=axs[0])\nsns.boxenplot(data=df['Number of Dependents'], color='#7ec837', ax=axs[1]);","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-01T17:14:01.988008Z","iopub.execute_input":"2024-12-01T17:14:01.988446Z","iopub.status.idle":"2024-12-01T17:14:03.532603Z","shell.execute_reply.started":"2024-12-01T17:14:01.988376Z","shell.execute_reply":"2024-12-01T17:14:03.531648Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Health score πͺπ»","metadata":{}},{"cell_type":"code","source":"df['Health Score'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:32:06.42397Z","iopub.execute_input":"2024-12-02T02:32:06.424392Z","iopub.status.idle":"2024-12-02T02:32:06.495412Z","shell.execute_reply.started":"2024-12-02T02:32:06.424354Z","shell.execute_reply":"2024-12-02T02:32:06.49414Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"_, axs = plt.subplots(1, 2, figsize=(12,6))\nsns.histplot(df['Health Score'], color='#377ec8', bins=75, ax=axs[0])\naxs[0].set_title(f\"mean: {np.round(df['Health Score'].mean(), 2)}, variance: {np.round(df['Health Score'].var(), 2)}\")\nsns.boxenplot(data=df['Health Score'], color='#7ec837', ax=axs[1]);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:32:13.481294Z","iopub.execute_input":"2024-12-02T02:32:13.481699Z","iopub.status.idle":"2024-12-02T02:32:15.36505Z","shell.execute_reply.started":"2024-12-02T02:32:13.48166Z","shell.execute_reply":"2024-12-02T02:32:15.363848Z"},"_kg_hide-input":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Previous Claims π\nThe majority of the data have either zero or one previous claims, and even more don't provide this information. There are some outliers, too, with very few have 6 or more claims.","metadata":{}},{"cell_type":"code","source":"df['Previous Claims'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:39:07.392751Z","iopub.execute_input":"2024-12-02T02:39:07.393292Z","iopub.status.idle":"2024-12-02T02:39:07.440988Z","shell.execute_reply.started":"2024-12-02T02:39:07.393241Z","shell.execute_reply":"2024-12-02T02:39:07.439845Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"_, axs = plt.subplots(1, 2, figsize=(12,6))\nsns.histplot(df['Previous Claims'], discrete=True, color='#377ec8', ax=axs[0])\nsns.boxplot(data=df['Previous Claims'], color='#7ec837', ax=axs[1]);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:35:16.534187Z","iopub.execute_input":"2024-12-02T02:35:16.534611Z","iopub.status.idle":"2024-12-02T02:35:17.915483Z","shell.execute_reply.started":"2024-12-02T02:35:16.534571Z","shell.execute_reply":"2024-12-02T02:35:17.914054Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":" This is the histogram and boxplot of the variable, including NAs.","metadata":{}},{"cell_type":"code","source":"_, axs = plt.subplots(1, 2, figsize=(12,6))\nsns.histplot(df['Previous Claims'].fill_null(-1), discrete=True, color='#377ec8', ax=axs[0])\nsns.boxplot(data=df['Previous Claims'].fill_null(-1), color='#7ec837', ax=axs[1]);","metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-12-02T02:36:24.35703Z","iopub.execute_input":"2024-12-02T02:36:24.357417Z","iopub.status.idle":"2024-12-02T02:36:25.588309Z","shell.execute_reply.started":"2024-12-02T02:36:24.35738Z","shell.execute_reply":"2024-12-02T02:36:25.586887Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Vehicle Age ππ΅\n","metadata":{}},{"cell_type":"code","source":"df['Vehicle Age'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:39:55.641789Z","iopub.execute_input":"2024-12-02T02:39:55.642198Z","iopub.status.idle":"2024-12-02T02:39:55.688659Z","shell.execute_reply.started":"2024-12-02T02:39:55.642159Z","shell.execute_reply":"2024-12-02T02:39:55.687315Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"_, axs = plt.subplots(1, 2, figsize=(12,6))\nsns.histplot(df['Vehicle Age'], discrete=True, color='#377ec8', ax=axs[0])\nsns.boxenplot(data=df['Vehicle Age'], color='#7cc837', ax=axs[1]);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-01T17:24:53.431082Z","iopub.execute_input":"2024-12-01T17:24:53.432041Z","iopub.status.idle":"2024-12-01T17:24:55.013081Z","shell.execute_reply.started":"2024-12-01T17:24:53.431997Z","shell.execute_reply":"2024-12-01T17:24:55.011924Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Credit Score π―\n The credit score is slightly left-skewed.","metadata":{}},{"cell_type":"code","source":"df['Credit Score'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:41:35.481557Z","iopub.execute_input":"2024-12-02T02:41:35.482Z","iopub.status.idle":"2024-12-02T02:41:35.544132Z","shell.execute_reply.started":"2024-12-02T02:41:35.481958Z","shell.execute_reply":"2024-12-02T02:41:35.54216Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"_, axs = plt.subplots(1,2, figsize=(12,6))\nsns.histplot(df['Credit Score'], bins=50, color='#377ec8', ax=axs[0])\nsns.boxplot(data=df['Credit Score'], color='#7cc837', ax=axs[1]);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:41:49.155275Z","iopub.execute_input":"2024-12-02T02:41:49.155667Z","iopub.status.idle":"2024-12-02T02:41:50.59398Z","shell.execute_reply.started":"2024-12-02T02:41:49.155631Z","shell.execute_reply":"2024-12-02T02:41:50.592691Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Insurance Duration β³οΈ\n The distribution of this data is uniform. The duration is in years.","metadata":{}},{"cell_type":"code","source":"df['Insurance Duration'].describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:44:02.354738Z","iopub.execute_input":"2024-12-02T02:44:02.357104Z","iopub.status.idle":"2024-12-02T02:44:02.424499Z","shell.execute_reply.started":"2024-12-02T02:44:02.357024Z","shell.execute_reply":"2024-12-02T02:44:02.422873Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"_, axs = plt.subplots(1,2, figsize=(12,6))\nsns.histplot(df['Insurance Duration'], discrete=True, color='#377ec8', ax=axs[0])\nsns.boxplot(data=df['Insurance Duration'], color='#7ec837', ax=axs[1]);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:44:05.508978Z","iopub.execute_input":"2024-12-02T02:44:05.509383Z","iopub.status.idle":"2024-12-02T02:44:06.870703Z","shell.execute_reply.started":"2024-12-02T02:44:05.509346Z","shell.execute_reply":"2024-12-02T02:44:06.869372Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Correlation of the Numeric Features\n Looking at the pairplots of the numeric features, it seems like there's no multicorrelation between the regressors. Furthermore, there is no clear correlation between `Premium Amount` and each the numeric regressors alone.","metadata":{}},{"cell_type":"code","source":"sns.pairplot(df[['Age', 'Annual Income', 'Health Score', 'Number of Dependents', 'Vehicle Age', 'Premium Amount']].to_pandas(), \n corner=True);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T02:48:25.103271Z","iopub.execute_input":"2024-12-02T02:48:25.103662Z","iopub.status.idle":"2024-12-02T02:49:40.011941Z","shell.execute_reply.started":"2024-12-02T02:48:25.103625Z","shell.execute_reply":"2024-12-02T02:49:40.010474Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Categorical Features\n We have several categorical variables in this dataset, such as `Age`, `Education Level`, just to name a few. **All of them have a uniform marginal distribution** (excluding the NAs). ","metadata":{}},{"cell_type":"code","source":"freq = df['Gender'].value_counts()\nplt.bar(freq['Gender'], freq['count'], color='#377ec8')\nplt.title('Gender');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:15:26.346151Z","iopub.execute_input":"2024-12-02T03:15:26.346517Z","iopub.status.idle":"2024-12-02T03:15:26.607284Z","shell.execute_reply.started":"2024-12-02T03:15:26.346485Z","shell.execute_reply":"2024-12-02T03:15:26.606058Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"freq = df['Marital Status'].fill_null(\"Missing\").value_counts()\nplt.bar(freq['Marital Status'], freq['count'], color='#377ec8')\nplt.title('Marital Status');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:15:00.121869Z","iopub.execute_input":"2024-12-02T03:15:00.122241Z","iopub.status.idle":"2024-12-02T03:15:00.471014Z","shell.execute_reply.started":"2024-12-02T03:15:00.12221Z","shell.execute_reply":"2024-12-02T03:15:00.469848Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"freq = df['Education Level'].value_counts()\nplt.bar(freq['Education Level'], freq['count'], color='#377ec8')\nplt.title('Education Level');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:14:27.270865Z","iopub.execute_input":"2024-12-02T03:14:27.271375Z","iopub.status.idle":"2024-12-02T03:14:27.559515Z","shell.execute_reply.started":"2024-12-02T03:14:27.271327Z","shell.execute_reply":"2024-12-02T03:14:27.558359Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"freq = df['Occupation'].fill_null(\"Missing\").value_counts()\nplt.bar(freq['Occupation'], freq['count'], color='#377ec8')\nplt.title('Occupation');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:13:39.97865Z","iopub.execute_input":"2024-12-02T03:13:39.979053Z","iopub.status.idle":"2024-12-02T03:13:40.350074Z","shell.execute_reply.started":"2024-12-02T03:13:39.979018Z","shell.execute_reply":"2024-12-02T03:13:40.348886Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"freq = df['Location'].fill_null(\"Missing\").value_counts()\nplt.bar(freq['Location'], freq['count'], color='#377ec8')\nplt.title('Location');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:13:02.78273Z","iopub.execute_input":"2024-12-02T03:13:02.783149Z","iopub.status.idle":"2024-12-02T03:13:03.069158Z","shell.execute_reply.started":"2024-12-02T03:13:02.783113Z","shell.execute_reply":"2024-12-02T03:13:03.068004Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"freq = df['Policy Type'].fill_null(\"Missing\").value_counts()\nplt.bar(freq['Policy Type'], freq['count'], color='#377ec8')\nplt.title('Policy Type');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:12:32.717822Z","iopub.execute_input":"2024-12-02T03:12:32.718248Z","iopub.status.idle":"2024-12-02T03:12:33.084076Z","shell.execute_reply.started":"2024-12-02T03:12:32.718213Z","shell.execute_reply":"2024-12-02T03:12:33.082846Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"policy_start_date = df['Policy Start Date'].str.to_datetime()\nmonth_start = policy_start_date.dt.month()\nyear_start = policy_start_date.dt.year()\n\n\n_, axs = plt.subplots(1, 2, figsize=(12,6))\nsns.histplot(month_start, discrete=True, color='#377ec8', ax=axs[0])\naxs[0].set_title('Month start')\nsns.histplot(year_start, discrete=True, color='#7ec837', ax=axs[1])\naxs[1].set_title('Year start');","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-02T03:08:15.381744Z","iopub.execute_input":"2024-12-02T03:08:15.382659Z","iopub.status.idle":"2024-12-02T03:08:17.882408Z","shell.execute_reply.started":"2024-12-02T03:08:15.382609Z","shell.execute_reply":"2024-12-02T03:08:17.881217Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"freq = df['Customer Feedback'].fill_null(\"Missing\").value_counts()\nplt.bar(freq['Customer Feedback'], freq['count'], color='#377ec8')\nplt.title('Customer Feedback');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:15:55.92653Z","iopub.execute_input":"2024-12-02T03:15:55.92697Z","iopub.status.idle":"2024-12-02T03:15:56.275069Z","shell.execute_reply.started":"2024-12-02T03:15:55.926932Z","shell.execute_reply":"2024-12-02T03:15:56.27396Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"freq = df['Smoking Status'].fill_null(\"Missing\").value_counts()\nplt.bar(freq['Smoking Status'], freq['count'], color='#377ec8')\nplt.title('Smoking Status');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:16:12.499749Z","iopub.execute_input":"2024-12-02T03:16:12.500177Z","iopub.status.idle":"2024-12-02T03:16:12.75443Z","shell.execute_reply.started":"2024-12-02T03:16:12.500143Z","shell.execute_reply":"2024-12-02T03:16:12.75329Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"freq = df['Exercise Frequency'].fill_null(\"Missing\").value_counts()\nplt.bar(freq['Exercise Frequency'], freq['count'], color='#377ec8')\nplt.title('Exercise Frequency');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:16:29.503633Z","iopub.execute_input":"2024-12-02T03:16:29.504119Z","iopub.status.idle":"2024-12-02T03:16:29.854617Z","shell.execute_reply.started":"2024-12-02T03:16:29.504082Z","shell.execute_reply":"2024-12-02T03:16:29.853133Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"freq = df['Property Type'].fill_null(\"Missing\").value_counts()\nplt.bar(freq['Property Type'], freq['count'], color='#377ec8')\nplt.title('Property Type');","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-12-02T03:16:41.672088Z","iopub.execute_input":"2024-12-02T03:16:41.672492Z","iopub.status.idle":"2024-12-02T03:16:41.970602Z","shell.execute_reply.started":"2024-12-02T03:16:41.672456Z","shell.execute_reply":"2024-12-02T03:16:41.96932Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":" This is a work-in-progress notebook, so I will add more insights.\n\n Thank you so much for checking out this notebook!","metadata":{}}]}