{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-12-21T17:53:57.281729Z","iopub.execute_input":"2022-12-21T17:53:57.282157Z","iopub.status.idle":"2022-12-21T17:55:56.951904Z","shell.execute_reply.started":"2022-12-21T17:53:57.282128Z","shell.execute_reply":"2022-12-21T17:55:56.949928Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport PIL","metadata":{"execution":{"iopub.status.busy":"2022-12-21T17:57:13.042884Z","iopub.execute_input":"2022-12-21T17:57:13.044402Z","iopub.status.idle":"2022-12-21T17:57:13.050524Z","shell.execute_reply.started":"2022-12-21T17:57:13.044337Z","shell.execute_reply":"2022-12-21T17:57:13.048464Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train=pd.read_csv(\"../input/happy-whale-and-dolphin/train.csv\")\ntest=pd.read_csv(\"../input/happy-whale-and-dolphin/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-12-21T17:57:14.974193Z","iopub.execute_input":"2022-12-21T17:57:14.974595Z","iopub.status.idle":"2022-12-21T17:57:15.163608Z","shell.execute_reply.started":"2022-12-21T17:57:14.974561Z","shell.execute_reply":"2022-12-21T17:57:15.161912Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.info()","metadata":{"execution":{"iopub.status.busy":"2022-12-21T17:57:17.801854Z","iopub.execute_input":"2022-12-21T17:57:17.802248Z","iopub.status.idle":"2022-12-21T17:57:17.837644Z","shell.execute_reply.started":"2022-12-21T17:57:17.802214Z","shell.execute_reply":"2022-12-21T17:57:17.835985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-21T17:57:20.59379Z","iopub.execute_input":"2022-12-21T17:57:20.594248Z","iopub.status.idle":"2022-12-21T17:57:20.612995Z","shell.execute_reply.started":"2022-12-21T17:57:20.594212Z","shell.execute_reply":"2022-12-21T17:57:20.612087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"SpeciesCount=sns.countplot(x='species',data=train,palette='rainbow')\nplt.xticks(rotation=90)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-12-21T17:57:24.626827Z","iopub.execute_input":"2022-12-21T17:57:24.628465Z","iopub.status.idle":"2022-12-21T17:57:24.999967Z","shell.execute_reply.started":"2022-12-21T17:57:24.628401Z","shell.execute_reply":"2022-12-21T17:57:24.998668Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['species'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-12-21T17:57:28.208377Z","iopub.execute_input":"2022-12-21T17:57:28.208815Z","iopub.status.idle":"2022-12-21T17:57:28.224398Z","shell.execute_reply.started":"2022-12-21T17:57:28.208777Z","shell.execute_reply":"2022-12-21T17:57:28.222472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Exploratory Analysis**","metadata":{}},{"cell_type":"markdown","source":"Checking size of images and if not same then making it of same dimensions","metadata":{}},{"cell_type":"code","source":"image_list=train['image'].to_list()\nfor i in range (0,len(image_list)):\n   image_list[i]='/kaggle/input/happy-whale-and-dolphin/train_images/'+image_list[i]","metadata":{"execution":{"iopub.status.busy":"2022-12-21T18:30:41.220672Z","iopub.execute_input":"2022-12-21T18:30:41.221103Z","iopub.status.idle":"2022-12-21T18:30:41.240073Z","shell.execute_reply.started":"2022-12-21T18:30:41.221068Z","shell.execute_reply":"2022-12-21T18:30:41.238548Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from PIL import Image\nimport cv2 #for resizing\n\nfor i in image_list:\n    img=Image.open(i)\n    #print(img.size)\n#sizes are found to be different. So, to make them uniform\nres_images=[]\nfor i in image_list:\n    res_img= cv2.resize(i,)\n\n    ","metadata":{"execution":{"iopub.status.busy":"2022-12-21T18:32:19.216791Z","iopub.execute_input":"2022-12-21T18:32:19.217202Z","iopub.status.idle":"2022-12-21T18:33:02.689719Z","shell.execute_reply.started":"2022-12-21T18:32:19.217168Z","shell.execute_reply":"2022-12-21T18:33:02.688073Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}}]}