{"cells":[{"metadata":{},"cell_type":"markdown","source":"# 🕵 ** A Statistical analysis on Pulmonary Fibrosis ** 🕵\n","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"## 🔥**This is my first kernal in the image processing space.** 🔥 \n\n## **Hope you will read something new and interesting out here.** 😊😊","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"
\n \n
","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"##

Contents of Kernal \n \n(click to navigate) \n\n\n## [1. Gender distribution](#1) \n## [2. Age Distribution](#2) \n## [3. Number of Visits](#3) \n## [4. FVC Frequency](#4) \n## [5. Age Vs FVC](#5) \n## [6. Smoking Status based on sex](#6) \n## [7. Comparing the all the data based on sex](#7) \n## [8. Age with the smoking status](#8) \n## [9. Distribution of Age with the sex](#9) \n## [10.Heat Map with all the data](#10) \n\n \n## [Key Take Aways](#takeaways) \n\n\n\n## Feel free to comment your thoughts and suggestions are welcomed!! ","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"## Loading libs","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nimport pandas as pd\n\n#ploting\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns\nsns.set(style=\"darkgrid\")\n\n#plotly\nimport plotly.express as px\n\n#color\nfrom colorama import Fore, Style,Back\n\n#pydicom\nimport pydicom\n\nplt.style.use(\"seaborn-notebook\")\nplt.show()\n\n\n# Suppress warnings \nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"root_path = '/kaggle/input/osic-pulmonary-fibrosis-progression/'","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Loading files ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train = pd.read_csv(os.path.join(root_path,\"train.csv\"))\ndf_test = pd.read_csv(os.path.join(root_path,\"test.csv\"))\nsubmission = pd.read_csv(os.path.join(root_path,\"sample_submission.csv\"))\ntrain_folder = root_path+'train/'\ntest_folder = root_path+'train/'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train.head().style.bar(subset=[\"FVC\"],color=['#F7DC6F'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train.info()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Visualization of data   [👆Back](#home)\n

1. Gender distribution

","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10,5))\na = sns.countplot(data=df_train,x=\"Sex\",hue=\"Sex\",color=\"blue\",palette=[\"#F5B041\",\"#58D68D\"])\nplt.title(\"Gender Distribution\")\nplt.legend(fontsize=10)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 2. Age Distribution   [👆Back](#home) \n","execution_count":null},{"metadata":{"_kg_hide-input":false,"trusted":true},"cell_type":"code","source":"a = df_train[\"Age\"].plot.hist(colormap=\"jet\",legend=True,color=\"#BB8FCE\")\nplt.xlabel(\"Age\")\nplt.legend(fontsize=10)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 3. Number of Visits   [👆Back](#home) \n","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"a = df_train[\"Patient\"].value_counts().plot.hist(legend=True,color=\"#85C1E9\")\nplt.xlabel(\"no of visits\")\nplt.legend(fontsize=10)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 4. FVC Frequency   [👆Back](#home) ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"a = df_train[\"FVC\"].plot.kde(legend=True,color=\"#F8C471\",linewidth=2.8)\nplt.legend(fontsize=10)\nplt.xlabel(\"FVC\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('The mean value of FCV is',Back.CYAN+Style.BRIGHT+Fore.BLACK, f'{df_train[\"FVC\"].mean()}')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 5. Age Vs FVC   [👆Back](#home) ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"a = df_train.plot.scatter(x=\"FVC\",y=\"Age\",color=\"#E74C3C\")\nplt.legend(fontsize=10)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 6. Smoking Status based on sex   [👆Back](#home) ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(45,8))\nsns.set(font_scale=1.1)\nax = sns.catplot(x=\"SmokingStatus\", hue=\"Sex\", col=\"Sex\",data=df_train, kind=\"count\",palette=[\"#F8C471\",\"#58D68D\"])\nplt.legend(fontsize=10)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 7. Comparing the all the data based on sex   [👆Back](#home) ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.pairplot(df_train, hue=\"Sex\", palette=\"Set2\", diag_kind=\"kde\", height=2.5)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 8. Age with the smoking status   [👆Back](#home) ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"# sns.violinplot(x=\"SmokingStatus\", y=\"Age\", data=df_train, size=7,palette=[\"#F5B7B1\",\"#2ECC71\",\"#E74C3C\"])\nfig = px.violin(df_train, y=\"Age\", x=\"SmokingStatus\", color=\"Sex\", box=True, points=\"all\")\nfig.update_layout(\n autosize=False,\n width=1200,\n height=700,)\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 9. Distribution of Age with the sex   [👆Back](#home) ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.violin(df_train, y=\"Age\", x=\"Sex\", color=\"Sex\", box=True, points=\"all\")\nfig.update_layout(\n autosize=False,\n width=1000,\n height=700,)\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 10. Heat Map with all the data   [👆Back](#home) ","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train_corr = df_train.corr()\nsns.clustermap(df_train_corr, cmap=\"GnBu\",annot=True)\nplt.legend(fontsize=10)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":" \n

Key Take aways

\n

\n 1. Age ranges from 60 to 75 with FVC from 1500 to 3000

\n 2. People have an average of 9 visits

\n 3. Most people are in the age range from 65 to 75

\n 4. The number of male out range female

\n 5. In both the gender, the number of current smokers is less than never smokers

\n 6. Most male smokers are aged between 65 and 70

\n 7. There exists a good correlation between the FVC value and Percent

\n

\n\n

✨✨ Thanks for reading out till the end ✨✨

\n\n

Let's me know what can be improved in the comments

If you liked it please do consider upvoting!!😎

Thanks in advance 😊😊

\n\n# [👆Back to top 👆](#home)\n\n \n## ⚙ ToDo ⚙\n## * Image Analysis\n## * Model Building\n## * Model Training\n \n","execution_count":null}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}