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{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import seaborn as sns\nfrom sklearn.model_selection import KFold\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd \nfrom tqdm import tqdm\nfrom sklearn.preprocessing import OrdinalEncoder, LabelEncoder, StandardScaler\nfrom sklearn.model_selection import train_test_split, RandomizedSearchCV\nfrom sklearn.metrics import accuracy_score, make_scorer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier, AdaBoostClassifier, IsolationForest, StackingClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom xgboost import XGBClassifier\nfrom catboost import CatBoostClassifier\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import StratifiedKFold\n\n\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input/titanic'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-02-14T16:28:46.348444Z","iopub.execute_input":"2023-02-14T16:28:46.349476Z","iopub.status.idle":"2023-02-14T16:28:47.385632Z","shell.execute_reply.started":"2023-02-14T16:28:46.349385Z","shell.execute_reply":"2023-02-14T16:28:47.383885Z"},"trusted":true},"execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<style type='text/css'>\n.datatable table.frame { margin-bottom: 0; }\n.datatable table.frame thead { border-bottom: none; }\n.datatable table.frame tr.coltypes td { color: #FFFFFF; line-height: 6px; padding: 0 0.5em;}\n.datatable .bool { background: #DDDD99; }\n.datatable .object { background: #565656; }\n.datatable .int { background: #5D9E5D; }\n.datatable .float { background: #4040CC; }\n.datatable .str { background: #CC4040; }\n.datatable .time { background: #40CC40; }\n.datatable .row_index { background: var(--jp-border-color3); border-right: 1px solid var(--jp-border-color0); color: var(--jp-ui-font-color3); font-size: 9px;}\n.datatable .frame tbody td { text-align: left; }\n.datatable .frame tr.coltypes .row_index { background: var(--jp-border-color0);}\n.datatable th:nth-child(2) { padding-left: 12px; }\n.datatable .hellipsis { color: var(--jp-cell-editor-border-color);}\n.datatable .vellipsis { background: var(--jp-layout-color0); color: var(--jp-cell-editor-border-color);}\n.datatable .na { color: var(--jp-cell-editor-border-color); font-size: 80%;}\n.datatable .sp { opacity: 0.25;}\n.datatable .footer { font-size: 9px; }\n.datatable .frame_dimensions { background: var(--jp-border-color3); border-top: 1px solid var(--jp-border-color0); color: var(--jp-ui-font-color3); display: inline-block; opacity: 0.6; padding: 1px 10px 1px 5px;}\n</style>\n"},"metadata":{}},{"name":"stdout","text":"/kaggle/input/titanic/train.csv\n/kaggle/input/titanic/test.csv\n/kaggle/input/titanic/gender_submission.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/titanic/train.csv')\ntest = pd.read_csv('/kaggle/input/titanic/test.csv')","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.393375Z","iopub.execute_input":"2023-02-14T16:28:47.393791Z","iopub.status.idle":"2023-02-14T16:28:47.412839Z","shell.execute_reply.started":"2023-02-14T16:28:47.393752Z","shell.execute_reply":"2023-02-14T16:28:47.411020Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"#### Printing the value counts for the train set","metadata":{}},{"cell_type":"code","source":"complete","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:49:33.151322Z","iopub.execute_input":"2023-02-14T16:49:33.151797Z","iopub.status.idle":"2023-02-14T16:49:33.178423Z","shell.execute_reply.started":"2023-02-14T16:49:33.151768Z","shell.execute_reply":"2023-02-14T16:49:33.176421Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":" Survived Pclass Sex Age SibSp Parch Fare \\\nPassengerId \n1 0.0 3 male 22.000000 1 0 7.2500 \n2 1.0 1 female 38.000000 1 0 71.2833 \n3 1.0 3 female 26.000000 0 0 7.9250 \n4 1.0 1 female 35.000000 1 0 53.1000 \n5 0.0 3 male 35.000000 0 0 8.0500 \n... ... ... ... ... ... ... ... \n1305 NaN 3 male 29.881138 0 0 8.0500 \n1306 NaN 1 female 39.000000 0 0 108.9000 \n1307 NaN 3 male 38.500000 0 0 7.2500 \n1308 NaN 3 male 29.881138 0 0 8.0500 \n1309 NaN 3 male 29.881138 1 1 22.3583 \n\n Embarked \nPassengerId \n1 S \n2 C \n3 S \n4 S \n5 S \n... ... \n1305 S \n1306 C \n1307 S \n1308 S \n1309 C \n\n[1309 rows x 8 columns]","text/html":"<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Fare</th>\n <th>Embarked</th>\n </tr>\n <tr>\n <th>PassengerId</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>0.0</td>\n <td>3</td>\n <td>male</td>\n <td>22.000000</td>\n <td>1</td>\n <td>0</td>\n <td>7.2500</td>\n <td>S</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1.0</td>\n <td>1</td>\n <td>female</td>\n <td>38.000000</td>\n <td>1</td>\n <td>0</td>\n <td>71.2833</td>\n <td>C</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1.0</td>\n <td>3</td>\n <td>female</td>\n <td>26.000000</td>\n <td>0</td>\n <td>0</td>\n <td>7.9250</td>\n <td>S</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1.0</td>\n <td>1</td>\n <td>female</td>\n <td>35.000000</td>\n <td>1</td>\n <td>0</td>\n <td>53.1000</td>\n <td>S</td>\n </tr>\n <tr>\n <th>5</th>\n <td>0.0</td>\n <td>3</td>\n <td>male</td>\n <td>35.000000</td>\n <td>0</td>\n <td>0</td>\n <td>8.0500</td>\n <td>S</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1305</th>\n <td>NaN</td>\n <td>3</td>\n <td>male</td>\n <td>29.881138</td>\n <td>0</td>\n <td>0</td>\n <td>8.0500</td>\n <td>S</td>\n </tr>\n <tr>\n <th>1306</th>\n <td>NaN</td>\n <td>1</td>\n <td>female</td>\n <td>39.000000</td>\n <td>0</td>\n <td>0</td>\n <td>108.9000</td>\n <td>C</td>\n </tr>\n <tr>\n <th>1307</th>\n <td>NaN</td>\n <td>3</td>\n <td>male</td>\n <td>38.500000</td>\n <td>0</td>\n <td>0</td>\n <td>7.2500</td>\n <td>S</td>\n </tr>\n <tr>\n <th>1308</th>\n <td>NaN</td>\n <td>3</td>\n <td>male</td>\n <td>29.881138</td>\n <td>0</td>\n <td>0</td>\n <td>8.0500</td>\n <td>S</td>\n </tr>\n <tr>\n <th>1309</th>\n <td>NaN</td>\n <td>3</td>\n <td>male</td>\n <td>29.881138</td>\n <td>1</td>\n <td>1</td>\n <td>22.3583</td>\n <td>C</td>\n </tr>\n </tbody>\n</table>\n<p>1309 rows × 8 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"for i in train.columns:\n print(train[i].value_counts())","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.414400Z","iopub.execute_input":"2023-02-14T16:28:47.415874Z","iopub.status.idle":"2023-02-14T16:28:47.438461Z","shell.execute_reply.started":"2023-02-14T16:28:47.415816Z","shell.execute_reply":"2023-02-14T16:28:47.437144Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"1 1\n599 1\n588 1\n589 1\n590 1\n ..\n301 1\n302 1\n303 1\n304 1\n891 1\nName: PassengerId, Length: 891, dtype: int64\n0 549\n1 342\nName: Survived, dtype: int64\n3 491\n1 216\n2 184\nName: Pclass, dtype: int64\nBraund, Mr. Owen Harris 1\nBoulos, Mr. Hanna 1\nFrolicher-Stehli, Mr. Maxmillian 1\nGilinski, Mr. Eliezer 1\nMurdlin, Mr. Joseph 1\n ..\nKelly, Miss. Anna Katherine \"Annie Kate\" 1\nMcCoy, Mr. Bernard 1\nJohnson, Mr. William Cahoone Jr 1\nKeane, Miss. Nora A 1\nDooley, Mr. Patrick 1\nName: Name, Length: 891, dtype: int64\nmale 577\nfemale 314\nName: Sex, dtype: int64\n24.00 30\n22.00 27\n18.00 26\n19.00 25\n28.00 25\n ..\n36.50 1\n55.50 1\n0.92 1\n23.50 1\n74.00 1\nName: Age, Length: 88, dtype: int64\n0 608\n1 209\n2 28\n4 18\n3 16\n8 7\n5 5\nName: SibSp, dtype: int64\n0 678\n1 118\n2 80\n5 5\n3 5\n4 4\n6 1\nName: Parch, dtype: int64\n347082 7\nCA. 2343 7\n1601 7\n3101295 6\nCA 2144 6\n ..\n9234 1\n19988 1\n2693 1\nPC 17612 1\n370376 1\nName: Ticket, Length: 681, dtype: int64\n8.0500 43\n13.0000 42\n7.8958 38\n7.7500 34\n26.0000 31\n ..\n35.0000 1\n28.5000 1\n6.2375 1\n14.0000 1\n10.5167 1\nName: Fare, Length: 248, dtype: int64\nB96 B98 4\nG6 4\nC23 C25 C27 4\nC22 C26 3\nF33 3\n ..\nE34 1\nC7 1\nC54 1\nE36 1\nC148 1\nName: Cabin, Length: 147, dtype: int64\nS 644\nC 168\nQ 77\nName: Embarked, dtype: int64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Joining the train and test data set","metadata":{}},{"cell_type":"code","source":"complete = pd.concat([train, test])","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.442381Z","iopub.execute_input":"2023-02-14T16:28:47.443178Z","iopub.status.idle":"2023-02-14T16:28:47.456140Z","shell.execute_reply.started":"2023-02-14T16:28:47.443129Z","shell.execute_reply":"2023-02-14T16:28:47.454821Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"#### Checking for null and duplicated values","metadata":{}},{"cell_type":"code","source":"complete.duplicated().sum()\ncomplete.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.457475Z","iopub.execute_input":"2023-02-14T16:28:47.457798Z","iopub.status.idle":"2023-02-14T16:28:47.478585Z","shell.execute_reply.started":"2023-02-14T16:28:47.457770Z","shell.execute_reply":"2023-02-14T16:28:47.477801Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"PassengerId 0\nSurvived 418\nPclass 0\nName 0\nSex 0\nAge 263\nSibSp 0\nParch 0\nTicket 0\nFare 1\nCabin 1014\nEmbarked 2\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Replacing the null values for Age and Fare with mean and null values for Embarked with mode.\n","metadata":{}},{"cell_type":"code","source":"complete['Age'] = complete['Age'].fillna(np.mean(complete.Age))\ncomplete['Fare'] = complete['Fare'].fillna(np.mean(complete.Fare))\ncomplete['Embarked'] = complete['Embarked'].fillna(complete.Embarked.mode()[0])\ncomplete.drop(columns = ['Cabin', 'Name', 'Ticket'], inplace = True)\ncomplete.set_index('PassengerId',inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.480195Z","iopub.execute_input":"2023-02-14T16:28:47.480500Z","iopub.status.idle":"2023-02-14T16:28:47.494223Z","shell.execute_reply.started":"2023-02-14T16:28:47.480473Z","shell.execute_reply":"2023-02-14T16:28:47.492468Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"train = complete[complete.Survived.notnull()]\ntest = complete[complete.Survived.isnull()]","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.497401Z","iopub.execute_input":"2023-02-14T16:28:47.497965Z","iopub.status.idle":"2023-02-14T16:28:47.510708Z","shell.execute_reply.started":"2023-02-14T16:28:47.497919Z","shell.execute_reply":"2023-02-14T16:28:47.509580Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"train.reset_index(inplace = True)\ntrain.drop(columns = 'PassengerId', inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.512174Z","iopub.execute_input":"2023-02-14T16:28:47.512866Z","iopub.status.idle":"2023-02-14T16:28:47.530639Z","shell.execute_reply.started":"2023-02-14T16:28:47.512769Z","shell.execute_reply":"2023-02-14T16:28:47.529204Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n errors=errors,\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Separating the predictors and traget variable","metadata":{}},{"cell_type":"code","source":"y = train.Survived\ntrain = train.drop(columns = 'Survived')\n","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.532027Z","iopub.execute_input":"2023-02-14T16:28:47.532487Z","iopub.status.idle":"2023-02-14T16:28:47.547140Z","shell.execute_reply.started":"2023-02-14T16:28:47.532449Z","shell.execute_reply":"2023-02-14T16:28:47.545978Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"#### Checking the distribution of the target variable","metadata":{}},{"cell_type":"code","source":"sns.countplot(x=y)","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.548137Z","iopub.execute_input":"2023-02-14T16:28:47.548464Z","iopub.status.idle":"2023-02-14T16:28:47.692831Z","shell.execute_reply.started":"2023-02-14T16:28:47.548438Z","shell.execute_reply":"2023-02-14T16:28:47.691891Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='Survived', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### Checking the distribution of categorical predictor variables","metadata":{}},{"cell_type":"code","source":"fig, axes = plt.subplots(nrows=2, ncols = 3, figsize=(10,10))\nfor i, ax in enumerate(axes.flat):\n if i< len(['Pclass', 'Sex', 'SibSp', 'Parch',\n 'Embarked']):\n sns.countplot(x= train[(['Pclass', 'Sex', 'SibSp', 'Parch',\n 'Embarked'][i])], ax= ax)\n ax.set_title(['Pclass', 'Sex', 'SibSp', 'Parch',\n 'Embarked'][i])\n else:\n axes.flat[i].set_visible(False)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:47.694435Z","iopub.execute_input":"2023-02-14T16:28:47.694966Z","iopub.status.idle":"2023-02-14T16:28:48.428565Z","shell.execute_reply.started":"2023-02-14T16:28:47.694932Z","shell.execute_reply":"2023-02-14T16:28:48.426893Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x720 with 6 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### Checking the distribution of continuous predictor variables","metadata":{}},{"cell_type":"code","source":"fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(12, 12))\n\nfor i, ax in enumerate(axes.flat):\n if i < len(['Age', 'Fare']):\n sns.histplot(train[(['Age', 'Fare'][i])], kde=True, ax=ax)\n ax.set_title(['Age', 'Fare'][i])\n else:\n axes.flat[i].set_visible(False)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:48.430218Z","iopub.execute_input":"2023-02-14T16:28:48.430546Z","iopub.status.idle":"2023-02-14T16:28:49.125984Z","shell.execute_reply.started":"2023-02-14T16:28:48.430515Z","shell.execute_reply":"2023-02-14T16:28:49.125279Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x864 with 3 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"#### One hot encoding","metadata":{}},{"cell_type":"code","source":"train = pd.get_dummies(train)","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:49.129199Z","iopub.execute_input":"2023-02-14T16:28:49.129465Z","iopub.status.idle":"2023-02-14T16:28:49.143425Z","shell.execute_reply.started":"2023-02-14T16:28:49.129443Z","shell.execute_reply":"2023-02-14T16:28:49.142066Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"#### Defining a function model_eval which uses RandomizedSearchCV for parameter tuning \n* Nested cross val used for training and testing to reduce the bias.\n* tqdm used to display a progress bar when performing parameter tuning.\n* Storing the score during each iteration and printing the mean score. ","metadata":{}},{"cell_type":"code","source":"def model_eval(model, param, train, y):\n outer_cv = KFold(n_splits = 5, shuffle = True, random_state = 0)\n inner_cv = KFold(n_splits = 5, shuffle = True, random_state = 0)\n random = RandomizedSearchCV(model, param, scoring = 'accuracy', n_jobs = -1, cv = inner_cv, random_state = 0)\n scores = []\n n_iter = random.n_iter\n with tqdm(total = n_iter) as pbar:\n for i in range(n_iter):\n random.set_params(n_iter = 1)\n for train_index, test_index in outer_cv.split(train, y):\n trainx, testx = train.iloc[train_index], train.iloc[test_index]\n trainy, testy = y[train_index], y[test_index]\n random.fit(trainx, trainy)\n scores.append(random.best_estimator_.score(testx, testy))\n pbar.update()\n print(random.best_params_)\n print('Average score', np.mean(scores))\n ","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:49.144828Z","iopub.execute_input":"2023-02-14T16:28:49.145466Z","iopub.status.idle":"2023-02-14T16:28:49.159012Z","shell.execute_reply.started":"2023-02-14T16:28:49.145431Z","shell.execute_reply":"2023-02-14T16:28:49.157277Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"### Decision Tree used for predictions\nMean accuracy of 80.1% was obtained on the test set.\n\nParameters tuned :\n* min_samples_split- min no. of samples required tosplit the node\n* max_depth- max depth of a tree\n* criterion- gini, entropy and log_loss are the three options available for classifier","metadata":{}},{"cell_type":"code","source":"model = DecisionTreeClassifier()\nmin_samples_split = np.array(range(1, 100))\nmax_depth= np.array(range(1,100))\ncriterion = ['entropy', 'gini', 'log_loss']\nparam = {'min_samples_split': min_samples_split, 'max_depth' : max_depth, 'criterion' : criterion}\nmodel_eval(model, param, train, y)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:49.161007Z","iopub.execute_input":"2023-02-14T16:28:49.161891Z","iopub.status.idle":"2023-02-14T16:28:52.772644Z","shell.execute_reply.started":"2023-02-14T16:28:49.161847Z","shell.execute_reply":"2023-02-14T16:28:52.771290Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stderr","text":"100%|██████████| 10/10 [00:03<00:00, 2.78it/s]","output_type":"stream"},{"name":"stdout","text":"{'min_samples_split': 60, 'max_depth': 28, 'criterion': 'entropy'}\nAverage score 0.8006879668570711\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Random Forest used for predcitions\nMean accuracy of 81.2% was obtained on the test set.\n\nParameters tuned :\n* min_samples_split- min no. of samples required tosplit the node\n* max_depth- max depth of a tree\n* criterion- gini, entropy and log_loss are the three options available for classifier\n* n_estimators- no. of trees in the forest","metadata":{}},{"cell_type":"code","source":"model = RandomForestClassifier()\nmin_samples_split = np.array(range(1, 100))\nmax_depth = np.array(range(1, 100))\ncriterion = ['gini', 'entropy']\nn_estimators = np.array(range(100, 400, 5))\nparam = {'min_samples_split': min_samples_split, 'max_depth': max_depth, 'criterion': criterion, 'n_estimators': n_estimators}\nmodel_eval(model, param, train, y)","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:28:52.774017Z","iopub.execute_input":"2023-02-14T16:28:52.775262Z","iopub.status.idle":"2023-02-14T16:29:32.242019Z","shell.execute_reply.started":"2023-02-14T16:28:52.775222Z","shell.execute_reply":"2023-02-14T16:29:32.240923Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stderr","text":"100%|██████████| 10/10 [00:39<00:00, 3.95s/it]","output_type":"stream"},{"name":"stdout","text":"{'n_estimators': 155, 'min_samples_split': 47, 'max_depth': 52, 'criterion': 'gini'}\nAverage score 0.811894419684891\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Extra Trees used for predcitions\nMean accuracy of 79.6% was obtained on the test set.\n\nParameters tuned :\n* min_samples_split- min no. of samples required tosplit the node\n* max_depth- max depth of a tree\n* criterion- gini, entropy and log_loss are the three options available for classifier\n* n_estimators- no. of trees in the forest","metadata":{}},{"cell_type":"code","source":"model = ExtraTreesClassifier()\ncriterion = ['gini', 'entropy']\nmax_depth = np.array(range(50, 150))\nmin_samples_split = np.array(range(1, 100))\nn_estimators = np.array(range(100,300, 2))\nparam = {'criterion': criterion, 'max_depth': max_depth, 'min_samples_split': min_samples_split, 'n_estimators': n_estimators}\nmodel_eval(model, param, train, y)\n","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:29:32.243517Z","iopub.execute_input":"2023-02-14T16:29:32.243846Z","iopub.status.idle":"2023-02-14T16:29:57.002277Z","shell.execute_reply.started":"2023-02-14T16:29:32.243816Z","shell.execute_reply":"2023-02-14T16:29:57.001351Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stderr","text":"100%|██████████| 10/10 [00:24<00:00, 2.47s/it]","output_type":"stream"},{"name":"stdout","text":"{'n_estimators': 122, 'min_samples_split': 88, 'max_depth': 80, 'criterion': 'gini'}\nAverage score 0.7957202937668696\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### GaussianNB used for prediction\nMean accuracy of 78.3% obtained in the test set.\n\nParameters tuned:\n* var_smoothing- Adds a value to the variance of the distribution of the train set","metadata":{}},{"cell_type":"code","source":"model = GaussianNB()\nparam = {'var_smoothing': np.logspace(0,-9, num=100)}\nmodel_eval(model, param, train, y)","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:29:57.003486Z","iopub.execute_input":"2023-02-14T16:29:57.003789Z","iopub.status.idle":"2023-02-14T16:29:59.100106Z","shell.execute_reply.started":"2023-02-14T16:29:57.003762Z","shell.execute_reply":"2023-02-14T16:29:59.099084Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","text":"100%|██████████| 10/10 [00:02<00:00, 4.79it/s]","output_type":"stream"},{"name":"stdout","text":"{'var_smoothing': 0.0001}\nAverage score 0.7833469336513715\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### SVC used for prediction\nMean accuracy of 78.7% obtained in the test set.\n\nParameters tuned:\n* C- Regularization parameter. The strength of the regularization is inversely propotional to C\n* gamma- control the shape of the decision boundary.Larger value implies overfitting.\n* kernel- poly, linear and rbf are selected ","metadata":{}},{"cell_type":"code","source":"model = SVC()\nparam = {'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100],'kernel': ['rbf', 'poly', 'linear'] }\nmodel_eval(model, param, train,y)","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:29:59.101395Z","iopub.execute_input":"2023-02-14T16:29:59.101683Z","iopub.status.idle":"2023-02-14T16:30:34.402505Z","shell.execute_reply.started":"2023-02-14T16:29:59.101656Z","shell.execute_reply":"2023-02-14T16:30:34.401530Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stderr","text":"100%|██████████| 10/10 [00:35<00:00, 3.53s/it]","output_type":"stream"},{"name":"stdout","text":"{'kernel': 'linear', 'gamma': 0.1, 'C': 0.1}\nAverage score 0.7867553825874082\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Bagging Classifier used for prediction\nMean accuracy of 83.3% obtained in the test set.\n\nParameters tuned:\n* n_estimators- no. of estimators\n* max_features- max no. of features ","metadata":{}},{"cell_type":"code","source":"model = BaggingClassifier()\nn_estimators = np.array(range(100,300,2))\nmax_features = np.array(range(1,20))\nparam = {'n_estimators': n_estimators, 'max_features': max_features}\nmodel_eval(model, param, train, y)","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:30:34.403950Z","iopub.execute_input":"2023-02-14T16:30:34.404245Z","iopub.status.idle":"2023-02-14T16:32:02.457156Z","shell.execute_reply.started":"2023-02-14T16:30:34.404219Z","shell.execute_reply":"2023-02-14T16:32:02.455373Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stderr","text":"100%|██████████| 10/10 [01:28<00:00, 8.80s/it]","output_type":"stream"},{"name":"stdout","text":"{'n_estimators': 268, 'max_features': 7}\nAverage score 0.8328585776159688\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### XGBoost used for prediction\nMean accuracy of 81.3% obtained in the test set.\n","metadata":{}},{"cell_type":"code","source":"model = XGBClassifier(objective='binary:logistic',\n eval_metric='error')\ncv = KFold(n_splits = 5, shuffle = True, random_state = 0)\nscores = []\nfor train_index, test_index in cv.split(train, y):\n trainx, testx = train.iloc[train_index], train.iloc[test_index]\n trainy, testy = y[train_index], y[test_index]\n model.fit(trainx, trainy)\n scores.append(model.score(testx, testy))\nprint('Average score', np.mean(scores))","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:32:02.458620Z","iopub.execute_input":"2023-02-14T16:32:02.458975Z","iopub.status.idle":"2023-02-14T16:32:03.494832Z","shell.execute_reply.started":"2023-02-14T16:32:02.458940Z","shell.execute_reply":"2023-02-14T16:32:03.494004Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Average score 0.8125290314481199\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### LGBM Boost used for prediction\nMean accuracy of 82.3% obtained in the test set.\n\nParameters tuned:\n* leatning_rate- learning rate of the model\n* max_depth- max_depth of the tree\n* alpha- l1 regularization\n* reg_lambda- l2 regularization\n* colsample_bytree- subsample ratio of columns when constructing each tree\n* subsample- subsample ratio\n* subsample_freq- frequency of the subsample\n* min_child_samples- min no. of data needed in a child(leaf)\n* n_estimators- no. of trees\n* num_leaves- max tree leaves","metadata":{}},{"cell_type":"code","source":"model = LGBMClassifier()\nlearning_rate = np.linspace(0.01, 0.2, 10)\nmax_depth = np.array(range(1, 10))\nalpha= np.array(range(0, 50))\nreg_lambda = np.linspace(0, 100, 10)\ncolsample_bytree = np.linspace(0.1,1, 10)\nsubsample = np.linspace(0.1, 1, 10)\nsubsample_freq = np.array(range(1, 10, 10))\nmin_child_samples = np.array(range(1,50,10))\nn_estimators = np.array(range(200, 2000,100 ))\nnum_leaves =np.array(range(2, 200, 11))\nparam= {'learning_rate': learning_rate, 'max_depth': max_depth, 'alpha': alpha, 'reg_lambda': reg_lambda, 'colsample_bytree': colsample_bytree,\n 'subsample': subsample, 'subsample_freq': subsample_freq, 'min_child_samples': min_child_samples, 'n_estimators': n_estimators,\n 'num_leaves': num_leaves}\nmodel_eval(model, param, train, y)","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:32:03.496033Z","iopub.execute_input":"2023-02-14T16:32:03.496967Z","iopub.status.idle":"2023-02-14T16:33:11.257865Z","shell.execute_reply.started":"2023-02-14T16:32:03.496929Z","shell.execute_reply":"2023-02-14T16:33:11.256212Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stderr","text":"100%|██████████| 10/10 [01:07<00:00, 6.77s/it]","output_type":"stream"},{"name":"stdout","text":"{'subsample_freq': 1, 'subsample': 1.0, 'reg_lambda': 33.33333333333333, 'num_leaves': 68, 'n_estimators': 1800, 'min_child_samples': 21, 'max_depth': 4, 'learning_rate': 0.1366666666666667, 'colsample_bytree': 0.5, 'alpha': 17}\nAverage score 0.8226476680685457\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Cat Boost used for prediction\nMean accuracy of 83.4% obtained in the test set.","metadata":{}},{"cell_type":"code","source":"model = CatBoostClassifier(verbose = 0)\ncv = KFold(n_splits = 5, shuffle = True, random_state = 0)\nscores = []\nfor train_index, test_index in cv.split(train, y):\n trainx, testx = train.iloc[train_index], train.iloc[test_index]\n trainy, testy = y[train_index], y[test_index]\n model.fit(trainx, trainy)\n scores.append(model.score(testx, testy))\nprint('Average score', np.mean(scores))\n","metadata":{"execution":{"iopub.status.busy":"2023-02-14T16:33:11.260230Z","iopub.execute_input":"2023-02-14T16:33:11.261140Z","iopub.status.idle":"2023-02-14T16:33:15.537556Z","shell.execute_reply.started":"2023-02-14T16:33:11.261101Z","shell.execute_reply":"2023-02-14T16:33:15.536433Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"Average score 0.8338773460548616\n","output_type":"stream"}]}]} | 0119/172/119172087.ipynb | s3://data-agents/kaggle-outputs/sharded/004_00119.jsonl.gz |
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/most-subscribed-1000-youtube-channels/topSubscribed.csv\n"
]
}
],
"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",
"\n",
"import numpy as np # linear algebra\n",
"import 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",
"\n",
"import os\n",
"for 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"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d7b47379",
"metadata": {
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},
"tags": []
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import plotly.express as px\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "edbb7e1c",
"metadata": {
"execution": {
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"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>Youtube Channel</th>\n",
" <th>Subscribers</th>\n",
" <th>Video Views</th>\n",
" <th>Video Count</th>\n",
" <th>Category</th>\n",
" <th>Started</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>T-Series</td>\n",
" <td>234,000,000</td>\n",
" <td>212,900,271,553</td>\n",
" <td>18,515</td>\n",
" <td>Music</td>\n",
" <td>2006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>YouTube Movies</td>\n",
" <td>161,000,000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Film & Animation</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Cocomelon - Nursery Rhymes</td>\n",
" <td>152,000,000</td>\n",
" <td>149,084,178,448</td>\n",
" <td>846</td>\n",
" <td>Education</td>\n",
" <td>2006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>SET India</td>\n",
" <td>150,000,000</td>\n",
" <td>137,828,094,104</td>\n",
" <td>103,200</td>\n",
" <td>Shows</td>\n",
" <td>2006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>MrBeast</td>\n",
" <td>128,000,000</td>\n",
" <td>21,549,128,785</td>\n",
" <td>733</td>\n",
" <td>Entertainment</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank Youtube Channel Subscribers Video Views Video Count \\\n",
"0 1 T-Series 234,000,000 212,900,271,553 18,515 \n",
"1 2 YouTube Movies 161,000,000 0 0 \n",
"2 3 Cocomelon - Nursery Rhymes 152,000,000 149,084,178,448 846 \n",
"3 4 SET India 150,000,000 137,828,094,104 103,200 \n",
"4 5 MrBeast 128,000,000 21,549,128,785 733 \n",
"\n",
" Category Started \n",
"0 Music 2006 \n",
"1 Film & Animation 2015 \n",
"2 Education 2006 \n",
"3 Shows 2006 \n",
"4 Entertainment 2012 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"../input/most-subscribed-1000-youtube-channels/topSubscribed.csv\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Rank 1000 non-null int64 \n",
" 1 Youtube Channel 1000 non-null object\n",
" 2 Subscribers 1000 non-null object\n",
" 3 Video Views 1000 non-null object\n",
" 4 Video Count 1000 non-null object\n",
" 5 Category 1000 non-null object\n",
" 6 Started 1000 non-null int64 \n",
"dtypes: int64(2), object(5)\n",
"memory usage: 54.8+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c3fc7d0f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.497545Z",
"iopub.status.busy": "2023-02-14T17:32:35.497118Z",
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},
"papermill": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(1000, 7)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Let's find how many rows and columns are there in our data\n",
"df.shape"
]
},
{
"cell_type": "markdown",
"id": "1fa0dde1",
"metadata": {
"papermill": {
"duration": 0.00698,
"end_time": "2023-02-14T17:32:35.522870",
"exception": false,
"start_time": "2023-02-14T17:32:35.515890",
"status": "completed"
},
"tags": []
},
"source": [
"# Clean and Prepare Data\n",
"Need to clean data in \"Subscribers\", \"Video Views\" and \"Video Count\" to remove \",\" and change its type to int for further analysis"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "49081204",
"metadata": {
"execution": {
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"start_time": "2023-02-14T17:32:35.530012",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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" <td>Cocomelon - Nursery Rhymes</td>\n",
" <td>152000000</td>\n",
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" <td>4</td>\n",
" <td>SET India</td>\n",
" <td>150000000</td>\n",
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" <td>103200</td>\n",
" <td>Shows</td>\n",
" <td>2006</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>MrBeast</td>\n",
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" <td>21549128785</td>\n",
" <td>733</td>\n",
" <td>Entertainment</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank Youtube Channel Subscribers Video Views Video Count \\\n",
"0 1 T-Series 234000000 212900271553 18515 \n",
"1 2 YouTube Movies 161000000 0 0 \n",
"2 3 Cocomelon - Nursery Rhymes 152000000 149084178448 846 \n",
"3 4 SET India 150000000 137828094104 103200 \n",
"4 5 MrBeast 128000000 21549128785 733 \n",
"\n",
" Category Started \n",
"0 Music 2006 \n",
"1 Film & Animation 2015 \n",
"2 Education 2006 \n",
"3 Shows 2006 \n",
"4 Entertainment 2012 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = [\"Subscribers\", \"Video Views\", \"Video Count\"]\n",
"for i in cols:\n",
" df[i] = df[i].str.replace(\",\",\"\")\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9ce8c384",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.577261Z",
"iopub.status.busy": "2023-02-14T17:32:35.576814Z",
"iopub.status.idle": "2023-02-14T17:32:35.585323Z",
"shell.execute_reply": "2023-02-14T17:32:35.584324Z"
},
"papermill": {
"duration": 0.01939,
"end_time": "2023-02-14T17:32:35.587770",
"exception": false,
"start_time": "2023-02-14T17:32:35.568380",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#changing the datatype to int64\n",
"for i in cols:\n",
" df[i] = df[i].astype(\"int64\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e6f46f10",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.604000Z",
"iopub.status.busy": "2023-02-14T17:32:35.603538Z",
"iopub.status.idle": "2023-02-14T17:32:35.619469Z",
"shell.execute_reply": "2023-02-14T17:32:35.618023Z"
},
"papermill": {
"duration": 0.02739,
"end_time": "2023-02-14T17:32:35.622345",
"exception": false,
"start_time": "2023-02-14T17:32:35.594955",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Rank 1000 non-null int64 \n",
" 1 Youtube Channel 1000 non-null object\n",
" 2 Subscribers 1000 non-null int64 \n",
" 3 Video Views 1000 non-null int64 \n",
" 4 Video Count 1000 non-null int64 \n",
" 5 Category 1000 non-null object\n",
" 6 Started 1000 non-null int64 \n",
"dtypes: int64(5), object(2)\n",
"memory usage: 54.8+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "01eefbe8",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.639402Z",
"iopub.status.busy": "2023-02-14T17:32:35.638962Z",
"iopub.status.idle": "2023-02-14T17:32:35.646208Z",
"shell.execute_reply": "2023-02-14T17:32:35.645312Z"
},
"papermill": {
"duration": 0.018734,
"end_time": "2023-02-14T17:32:35.648514",
"exception": false,
"start_time": "2023-02-14T17:32:35.629780",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Music', 'Film & Animation', 'Education', 'Shows', 'Entertainment',\n",
" 'https://us.youtubers.me/global/all/top-1000-most_subscribed-youtube-channels',\n",
" 'Gaming', 'People & Blogs', 'Sports', 'Howto & Style',\n",
" 'News & Politics', 'Comedy', 'Trailers', 'Nonprofits & Activism',\n",
" 'Science & Technology', 'Movies', 'Pets & Animals',\n",
" 'Autos & Vehicles', 'Travel & Events'], dtype=object)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Category'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "3f462187",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.664741Z",
"iopub.status.busy": "2023-02-14T17:32:35.664295Z",
"iopub.status.idle": "2023-02-14T17:32:35.672817Z",
"shell.execute_reply": "2023-02-14T17:32:35.671393Z"
},
"papermill": {
"duration": 0.020116,
"end_time": "2023-02-14T17:32:35.675879",
"exception": false,
"start_time": "2023-02-14T17:32:35.655763",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: The default value of regex will change from True to False in a future version.\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
}
],
"source": [
"#we have to replace the irregularities in the category column\n",
"\n",
"df['Category'] = df['Category'].str.replace('https://us.youtubers.me/global/all/top-1000-most_subscribed-youtube-channels', \"other\")\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7db32559",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.694197Z",
"iopub.status.busy": "2023-02-14T17:32:35.693748Z",
"iopub.status.idle": "2023-02-14T17:32:35.704969Z",
"shell.execute_reply": "2023-02-14T17:32:35.703536Z"
},
"papermill": {
"duration": 0.023295,
"end_time": "2023-02-14T17:32:35.707839",
"exception": false,
"start_time": "2023-02-14T17:32:35.684544",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Music', 'Film & animation', 'Education', 'Shows', 'Entertainment',\n",
" 'Other', 'Gaming', 'People & blogs', 'Sports', 'Howto & style',\n",
" 'News & politics', 'Comedy', 'Trailers', 'Nonprofits & activism',\n",
" 'Science & technology', 'Movies', 'Pets & animals',\n",
" 'Autos & vehicles', 'Travel & events'], dtype=object)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Category'] = df['Category'].str.capitalize()\n",
"df['Category'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b577441d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.724334Z",
"iopub.status.busy": "2023-02-14T17:32:35.723930Z",
"iopub.status.idle": "2023-02-14T17:32:35.738227Z",
"shell.execute_reply": "2023-02-14T17:32:35.737228Z"
},
"papermill": {
"duration": 0.025655,
"end_time": "2023-02-14T17:32:35.740967",
"exception": false,
"start_time": "2023-02-14T17:32:35.715312",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Rank 1000 non-null int64 \n",
" 1 Youtube Channel 1000 non-null object\n",
" 2 Subscribers 1000 non-null int64 \n",
" 3 Video Views 1000 non-null int64 \n",
" 4 Video Count 1000 non-null int64 \n",
" 5 Category 1000 non-null object\n",
" 6 Started 1000 non-null int64 \n",
"dtypes: int64(5), object(2)\n",
"memory usage: 54.8+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "bfd4bf3e",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.758763Z",
"iopub.status.busy": "2023-02-14T17:32:35.758312Z",
"iopub.status.idle": "2023-02-14T17:32:35.768161Z",
"shell.execute_reply": "2023-02-14T17:32:35.767205Z"
},
"papermill": {
"duration": 0.020994,
"end_time": "2023-02-14T17:32:35.770364",
"exception": false,
"start_time": "2023-02-14T17:32:35.749370",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Rank 0\n",
"Youtube Channel 0\n",
"Subscribers 0\n",
"Video Views 0\n",
"Video Count 0\n",
"Category 0\n",
"Started 0\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Check null values\n",
"\n",
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "3d5f2610",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.788310Z",
"iopub.status.busy": "2023-02-14T17:32:35.787191Z",
"iopub.status.idle": "2023-02-14T17:32:35.817267Z",
"shell.execute_reply": "2023-02-14T17:32:35.816001Z"
},
"papermill": {
"duration": 0.041896,
"end_time": "2023-02-14T17:32:35.819904",
"exception": false,
"start_time": "2023-02-14T17:32:35.778008",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>Subscribers</th>\n",
" <th>Video Views</th>\n",
" <th>Video Count</th>\n",
" <th>Started</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1000.000000</td>\n",
" <td>1.000000e+03</td>\n",
" <td>1.000000e+03</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>500.500000</td>\n",
" <td>2.158140e+07</td>\n",
" <td>9.994912e+09</td>\n",
" <td>9416.228000</td>\n",
" <td>2012.594000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>288.819436</td>\n",
" <td>1.662556e+07</td>\n",
" <td>1.300546e+10</td>\n",
" <td>32190.909114</td>\n",
" <td>4.110238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1.140000e+07</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>1970.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>250.750000</td>\n",
" <td>1.340000e+07</td>\n",
" <td>3.871470e+09</td>\n",
" <td>365.500000</td>\n",
" <td>2010.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>500.500000</td>\n",
" <td>1.660000e+07</td>\n",
" <td>6.723360e+09</td>\n",
" <td>896.000000</td>\n",
" <td>2013.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>750.250000</td>\n",
" <td>2.320000e+07</td>\n",
" <td>1.223052e+10</td>\n",
" <td>3277.250000</td>\n",
" <td>2015.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1000.000000</td>\n",
" <td>2.340000e+08</td>\n",
" <td>2.129003e+11</td>\n",
" <td>342802.000000</td>\n",
" <td>2021.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank Subscribers Video Views Video Count Started\n",
"count 1000.000000 1.000000e+03 1.000000e+03 1000.000000 1000.000000\n",
"mean 500.500000 2.158140e+07 9.994912e+09 9416.228000 2012.594000\n",
"std 288.819436 1.662556e+07 1.300546e+10 32190.909114 4.110238\n",
"min 1.000000 1.140000e+07 0.000000e+00 0.000000 1970.000000\n",
"25% 250.750000 1.340000e+07 3.871470e+09 365.500000 2010.000000\n",
"50% 500.500000 1.660000e+07 6.723360e+09 896.000000 2013.000000\n",
"75% 750.250000 2.320000e+07 1.223052e+10 3277.250000 2015.000000\n",
"max 1000.000000 2.340000e+08 2.129003e+11 342802.000000 2021.000000"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "96ff28ca",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.837469Z",
"iopub.status.busy": "2023-02-14T17:32:35.836988Z",
"iopub.status.idle": "2023-02-14T17:32:35.847165Z",
"shell.execute_reply": "2023-02-14T17:32:35.845969Z"
},
"papermill": {
"duration": 0.022141,
"end_time": "2023-02-14T17:32:35.849983",
"exception": false,
"start_time": "2023-02-14T17:32:35.827842",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Entertainment 238\n",
"Music 217\n",
"People & blogs 132\n",
"Gaming 94\n",
"Comedy 68\n",
"Film & animation 50\n",
"Education 45\n",
"Howto & style 43\n",
"Other 30\n",
"News & politics 27\n",
"Science & technology 18\n",
"Shows 14\n",
"Sports 10\n",
"Pets & animals 6\n",
"Trailers 2\n",
"Nonprofits & activism 2\n",
"Movies 2\n",
"Autos & vehicles 1\n",
"Travel & events 1\n",
"Name: Category, dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Category'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "68813bb8",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.868740Z",
"iopub.status.busy": "2023-02-14T17:32:35.868022Z",
"iopub.status.idle": "2023-02-14T17:32:35.878167Z",
"shell.execute_reply": "2023-02-14T17:32:35.876866Z"
},
"papermill": {
"duration": 0.022819,
"end_time": "2023-02-14T17:32:35.881059",
"exception": false,
"start_time": "2023-02-14T17:32:35.858240",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df.drop(df[df['Started'] == '1970'].index, axis = 0, inplace = True)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "26965c88",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.899799Z",
"iopub.status.busy": "2023-02-14T17:32:35.899338Z",
"iopub.status.idle": "2023-02-14T17:32:35.909003Z",
"shell.execute_reply": "2023-02-14T17:32:35.907660Z"
},
"papermill": {
"duration": 0.022465,
"end_time": "2023-02-14T17:32:35.911609",
"exception": false,
"start_time": "2023-02-14T17:32:35.889144",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"2014 120\n",
"2015 95\n",
"2013 88\n",
"2011 87\n",
"2012 81\n",
"2016 76\n",
"2006 69\n",
"2017 62\n",
"2009 59\n",
"2018 50\n",
"2008 45\n",
"2007 45\n",
"2010 45\n",
"2019 30\n",
"2005 21\n",
"2020 15\n",
"2021 11\n",
"1970 1\n",
"Name: Started, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Started'].value_counts()"
]
},
{
"cell_type": "markdown",
"id": "d0c15595",
"metadata": {
"papermill": {
"duration": 0.007701,
"end_time": "2023-02-14T17:32:35.927739",
"exception": false,
"start_time": "2023-02-14T17:32:35.920038",
"status": "completed"
},
"tags": []
},
"source": [
"# Data Visualization "
]
},
{
"cell_type": "markdown",
"id": "ae228e86",
"metadata": {
"papermill": {
"duration": 0.008133,
"end_time": "2023-02-14T17:32:35.944400",
"exception": false,
"start_time": "2023-02-14T17:32:35.936267",
"status": "completed"
},
"tags": []
},
"source": [
"# Number of Channels started over the years"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "a62b8564",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:35.963669Z",
"iopub.status.busy": "2023-02-14T17:32:35.963234Z",
"iopub.status.idle": "2023-02-14T17:32:37.333945Z",
"shell.execute_reply": "2023-02-14T17:32:37.332898Z"
},
"papermill": {
"duration": 1.383809,
"end_time": "2023-02-14T17:32:37.336605",
"exception": false,
"start_time": "2023-02-14T17:32:35.952796",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-2.18.0.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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},
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}
],
"source": [
"#Need to group data as per the year when the channel started\n",
"\n",
"var1 = df.groupby(['Started'])['Youtube Channel'].count()\n",
"var1 = var1.reset_index()\n",
"\n",
"px.bar(var1, x = 'Started', y = 'Youtube Channel')\n"
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},
{
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"id": "e151186e",
"metadata": {
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"exception": false,
"start_time": "2023-02-14T17:32:37.345389",
"status": "completed"
},
"tags": []
},
"source": [
"# Distribution of Subscribers among Categories"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "21180154",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-14T17:32:37.372663Z",
"iopub.status.busy": "2023-02-14T17:32:37.371941Z",
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},
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"start_time": "2023-02-14T17:32:37.362004",
"status": "completed"
},
"tags": []
},
"outputs": [
{
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"source": [
"var3 = df.groupby(['Category'])['Youtube Channel'].sum()\n",
"var3 = var3.reset_index()\n",
"px.pie(var3, values = 'Youtube Channel',names = 'Category' )\n"
]
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"source": [
"# Insights \n",
"\n",
"* Among the top 1000 Youtube channels 120 were started in the year of 2014, which was maximum for any given year in the given dataset.\n",
"* "
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