diff --git a/intern-basics/EEG_Alcohol/EEG_ALCOHOLIC_AJAY.ipynb b/intern-basics/EEG_Alcohol/EEG_ALCOHOLIC_AJAY.ipynb
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+++ b/intern-basics/EEG_Alcohol/EEG_ALCOHOLIC_AJAY.ipynb
@@ -0,0 +1 @@
+{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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 5GB 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","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"****CODE START****"},{"metadata":{},"cell_type":"markdown","source":"Import necessary libraries"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd \nimport os\nimport random\nfrom tqdm import tqdm\nimport matplotlib\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.graph_objs as go\nfrom plotly.offline import init_notebook_mode, iplot\nfrom plotly import tools\nfrom scipy.stats import mannwhitneyu\n\ninit_notebook_mode(connected=True) ## plotly init\nseed = 123\nrandom.seed = seed","execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {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-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"filenames_list = os.listdir('../input/Alcoholics/SMNI_CMI_TRAIN/Train/')\n\nData = pd.DataFrame({})\n\nfor file_names in tqdm(filenames_list):\n    temp_df = pd.read_csv('../input/Alcoholics/SMNI_CMI_TRAIN/Train/' + file_names)\n    Data = Data.append(temp_df)\n","execution_count":2,"outputs":[{"output_type":"stream","text":"100%|██████████| 468/468 [11:27<00:00,  1.47s/it]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Data.head(100000)","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"      Unnamed: 0  trial number sensor position  sample num  sensor value  \\\n0              5            34             FP1           0         2.360   \n1              6            34             FP1           1         2.848   \n2              7            34             FP1           2         1.383   \n3              8            34             FP1           3        -1.546   \n4              9            34             FP1           4        -3.499   \n...          ...           ...             ...         ...           ...   \n1691        1702            71              FZ         155         6.510   \n1692        1703            71              FZ         156         6.022   \n1693        1704            71              FZ         157         5.534   \n1694        1705            71              FZ         158         5.534   \n1695        1706            71              FZ         159         5.046   \n\n     subject identifier matching condition  channel         name      time  \n0                     c             S1 obj        0  co2c0000337  0.000000  \n1                     c             S1 obj        0  co2c0000337  0.003906  \n2                     c             S1 obj        0  co2c0000337  0.007812  \n3                     c             S1 obj        0  co2c0000337  0.011719  \n4                     c             S1 obj        0  co2c0000337  0.015625  \n...                 ...                ...      ...          ...       ...  \n1691                  a           S2 match        6  co2a0000368  0.605469  \n1692                  a           S2 match        6  co2a0000368  0.609375  \n1693                  a           S2 match        6  co2a0000368  0.613281  \n1694                  a           S2 match        6  co2a0000368  0.617188  \n1695                  a           S2 match        6  co2a0000368  0.621094  \n\n[100000 rows x 10 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>Unnamed: 0</th>\n      <th>trial number</th>\n      <th>sensor position</th>\n      <th>sample num</th>\n      <th>sensor value</th>\n      <th>subject identifier</th>\n      <th>matching condition</th>\n      <th>channel</th>\n      <th>name</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5</td>\n      <td>34</td>\n      <td>FP1</td>\n      <td>0</td>\n      <td>2.360</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>co2c0000337</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>34</td>\n      <td>FP1</td>\n      <td>1</td>\n      <td>2.848</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>co2c0000337</td>\n      <td>0.003906</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>34</td>\n      <td>FP1</td>\n      <td>2</td>\n      <td>1.383</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>co2c0000337</td>\n      <td>0.007812</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8</td>\n      <td>34</td>\n      <td>FP1</td>\n      <td>3</td>\n      <td>-1.546</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>co2c0000337</td>\n      <td>0.011719</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9</td>\n      <td>34</td>\n      <td>FP1</td>\n      <td>4</td>\n      <td>-3.499</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>co2c0000337</td>\n      <td>0.015625</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      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1691</th>\n      <td>1702</td>\n      <td>71</td>\n      <td>FZ</td>\n      <td>155</td>\n      <td>6.510</td>\n      <td>a</td>\n      <td>S2 match</td>\n      <td>6</td>\n      <td>co2a0000368</td>\n      <td>0.605469</td>\n    </tr>\n    <tr>\n      <th>1692</th>\n      <td>1703</td>\n      <td>71</td>\n      <td>FZ</td>\n      <td>156</td>\n      <td>6.022</td>\n      <td>a</td>\n      <td>S2 match</td>\n      <td>6</td>\n      <td>co2a0000368</td>\n      <td>0.609375</td>\n    </tr>\n    <tr>\n      <th>1693</th>\n      <td>1704</td>\n      <td>71</td>\n      <td>FZ</td>\n      <td>157</td>\n      <td>5.534</td>\n      <td>a</td>\n      <td>S2 match</td>\n      <td>6</td>\n      <td>co2a0000368</td>\n      <td>0.613281</td>\n    </tr>\n    <tr>\n      <th>1694</th>\n      <td>1705</td>\n      <td>71</td>\n      <td>FZ</td>\n      <td>158</td>\n      <td>5.534</td>\n      <td>a</td>\n      <td>S2 match</td>\n      <td>6</td>\n      <td>co2a0000368</td>\n      <td>0.617188</td>\n    </tr>\n    <tr>\n      <th>1695</th>\n      <td>1706</td>\n      <td>71</td>\n      <td>FZ</td>\n      <td>159</td>\n      <td>5.046</td>\n      <td>a</td>\n      <td>S2 match</td>\n      <td>6</td>\n      <td>co2a0000368</td>\n      <td>0.621094</td>\n    </tr>\n  </tbody>\n</table>\n<p>100000 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"**DATA STRUCTURE DETAILS**"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(Data.shape)\nprint(Data.dtypes)","execution_count":4,"outputs":[{"output_type":"stream","text":"(7667712, 10)\nUnnamed: 0              int64\ntrial number            int64\nsensor position        object\nsample num              int64\nsensor value          float64\nsubject identifier     object\nmatching condition     object\nchannel                 int64\nname                   object\ntime                  float64\ndtype: object\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"**DATA CLEAN CHECKING**"},{"metadata":{"trusted":true},"cell_type":"code","source":"for col in Data.columns:\n    pct_missing = np.mean(Data[col].isnull())\n    print('{} - {}%'.format(col, round(pct_missing*100)))","execution_count":5,"outputs":[{"output_type":"stream","text":"Unnamed: 0 - 0.0%\ntrial number - 0.0%\nsensor position - 0.0%\nsample num - 0.0%\nsensor value - 0.0%\nsubject identifier - 0.0%\nmatching condition - 0.0%\nchannel - 0.0%\nname - 0.0%\ntime - 0.0%\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"**Dropping necessary columns **"},{"metadata":{"trusted":true},"cell_type":"code","source":"Data=Data.drop(['Unnamed: 0','sensor position','name'],axis = 1) \n\n\n","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Importing Data Visualization Libraries seaborn and matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.violinplot(x=\"subject identifier\",y=\"sensor value\", hue=\"subject identifier\", data=Data[:1000],color='green'); #Selecting only first 500 columns from Dataset\nplt.show()","execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ax= sns.stripplot(x=\"channel\", y=\"matching condition\", data=Data[:1000],color=\"red\")","execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"g = sns.FacetGrid(data=Data[:1000],col='subject identifier')\ng.map(plt.hist,\"time\",color=\"green\")","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"<seaborn.axisgrid.FacetGrid at 0x7f51e4572910>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 216x216 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"g = sns.FacetGrid(data=Data[:500],col='subject identifier',hue='matching condition')\ng.map(plt.scatter,\"sensor value\",\"time\",alpha=0.7,color=\"yellow\")\ng.add_legend();","execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 320.5x216 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"# Plotting Pairwise Data Relationship"},{"metadata":{"trusted":true},"cell_type":"code","source":"Data_sample = Data[0:100]\n#Data_sample = Data_sample.drop(['trial number'],axis=1)\nData_sample.head()","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"   trial number  sample num  sensor value subject identifier  \\\n0            34           0         2.360                  c   \n1            34           1         2.848                  c   \n2            34           2         1.383                  c   \n3            34           3        -1.546                  c   \n4            34           4        -3.499                  c   \n\n  matching condition  channel      time  \n0             S1 obj        0  0.000000  \n1             S1 obj        0  0.003906  \n2             S1 obj        0  0.007812  \n3             S1 obj        0  0.011719  \n4             S1 obj        0  0.015625  ","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>trial number</th>\n      <th>sample num</th>\n      <th>sensor value</th>\n      <th>subject identifier</th>\n      <th>matching condition</th>\n      <th>channel</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>34</td>\n      <td>0</td>\n      <td>2.360</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>34</td>\n      <td>1</td>\n      <td>2.848</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>0.003906</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>34</td>\n      <td>2</td>\n      <td>1.383</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>0.007812</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>34</td>\n      <td>3</td>\n      <td>-1.546</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>0.011719</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>34</td>\n      <td>4</td>\n      <td>-3.499</td>\n      <td>c</td>\n      <td>S1 obj</td>\n      <td>0</td>\n      <td>0.015625</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"**These graph shows plots of all the selected attributes against each other**"},{"metadata":{"trusted":true},"cell_type":"code","source":"g = sns.PairGrid(Data_sample)\ng.map(plt.scatter ,color=\"orange\")","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"<seaborn.axisgrid.PairGrid at 0x7f51e4410350>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 900x900 with 25 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ng = sns.PairGrid(Data_sample, hue=\"subject identifier\")\ng.map_diag(plt.hist)\ng.map_offdiag(plt.scatter)\ng.add_legend()","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"<seaborn.axisgrid.PairGrid at 0x7f51d7712e90>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 992.625x900 with 30 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"#  Preprocessing of data"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Importing LabelEncoder\nfrom sklearn import preprocessing\nle = preprocessing.LabelEncoder()\n#Data['sensor position'] = le.fit_transform(Data['sensor position'])\nData['subject identifier'] = le.fit_transform(Data['subject identifier'])\nData['matching condition'] = le.fit_transform(Data['matching condition'])\n#Data['name'] = le.fit_transform(Data['name'])","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Data.head(100)","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"    trial number  sample num  sensor value  subject identifier  \\\n0             34           0         2.360                   1   \n1             34           1         2.848                   1   \n2             34           2         1.383                   1   \n3             34           3        -1.546                   1   \n4             34           4        -3.499                   1   \n..           ...         ...           ...                 ...   \n95            34          95         1.383                   1   \n96            34          96         0.407                   1   \n97            34          97        -0.570                   1   \n98            34          98        -1.546                   1   \n99            34          99        -3.988                   1   \n\n    matching condition  channel      time  \n0                    0        0  0.000000  \n1                    0        0  0.003906  \n2                    0        0  0.007812  \n3                    0        0  0.011719  \n4                    0        0  0.015625  \n..                 ...      ...       ...  \n95                   0        0  0.371094  \n96                   0        0  0.375000  \n97                   0        0  0.378906  \n98                   0        0  0.382812  \n99                   0        0  0.386719  \n\n[100 rows x 7 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>trial number</th>\n      <th>sample num</th>\n      <th>sensor value</th>\n      <th>subject identifier</th>\n      <th>matching condition</th>\n      <th>channel</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>34</td>\n      <td>0</td>\n      <td>2.360</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>34</td>\n      <td>1</td>\n      <td>2.848</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.003906</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>34</td>\n      <td>2</td>\n      <td>1.383</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.007812</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>34</td>\n      <td>3</td>\n      <td>-1.546</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.011719</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>34</td>\n      <td>4</td>\n      <td>-3.499</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.015625</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    </tr>\n    <tr>\n      <th>95</th>\n      <td>34</td>\n      <td>95</td>\n      <td>1.383</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.371094</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>34</td>\n      <td>96</td>\n      <td>0.407</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.375000</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>34</td>\n      <td>97</td>\n      <td>-0.570</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.378906</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>34</td>\n      <td>98</td>\n      <td>-1.546</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.382812</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>34</td>\n      <td>99</td>\n      <td>-3.988</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.386719</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 7 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Selection of data and target"},{"metadata":{"trusted":true},"cell_type":"code","source":"x= Data.iloc[:, Data.columns != 'subject identifier']\ny= Data.iloc[:, 3]\n# In the above code x represent data and y represent target","execution_count":21,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#import the necessary module to train-test split\n\nfrom sklearn.model_selection import train_test_split\nx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25)\nprint(\"Shape of data training set {}\".format(x_train.shape))    \nprint(\"Shape of data test set {}\".format(x_test.shape)) \nprint(\"Shape of target training set {}\".format(y_train.shape)) \nprint(\"Shape of target test set {}\".format(y_train.shape)) \n","execution_count":22,"outputs":[{"output_type":"stream","text":"Shape of data training set (5750784, 6)\nShape of data test set (1916928, 6)\nShape of target training set (5750784,)\nShape of target test set (5750784,)\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Various machine learning algorithms"},{"metadata":{},"cell_type":"markdown","source":"1. **Logistic Regression**"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\nlogmodel=LogisticRegression()\nlogmodel.fit(x_train,y_train)\ny_pred=logmodel.predict(x_test)\nprint(y_pred)\n\n\n\n#print the accuracy score\nprint(\"Logistic Regression Accuracy\",accuracy_score(y_test,y_pred,normalize = True))\n\n\n#code for confusion matrix\nconfusion_matrix(y_test,y_pred)\n\n#from yellowbrick.classifier import ClassificationReport\nfrom yellowbrick.classifier import ClassificationReport\n# Instantiate the classification model and visualizer\nvisualizer = ClassificationReport(logmodel, classes=['0','1'])\nvisualizer.fit(x_train, y_train) # Fit the training data to the visualizer\nvisualizer.score(x_test, y_test) # Evaluate the model on the test data\ng = visualizer.poof() # Draw/show/poof the data\n\n\n","execution_count":27,"outputs":[{"output_type":"stream","text":"[0 1 0 ... 1 0 1]\nLogistic Regression Accuracy 0.5578634147970085\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/base.py:213: FutureWarning:\n\nFrom version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 2 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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"**2.K-Nearest Neighbors**\n\n\nK nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure "},{"metadata":{"trusted":true},"cell_type":"code","source":"#import the necessary module\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\n\nlogmodel=KNeighborsClassifier()\nlogmodel.fit(x_train,y_train)\ny_pred=logmodel.predict(x_test)\nprint(y_pred)\n\n\n\n\n#print the accuracy score\nprint(\"K-Nearest Neighbors Accuracy\",accuracy_score(y_test,y_pred,normalize = True))\n\n\n#code for confusion matrix\nconfusion_matrix(y_test,y_pred)\n\nfrom yellowbrick.classifier import ClassificationReport\n# Instantiate the classification model and visualizer\nvisualizer = ClassificationReport(logmodel, classes=['0','1'])\nvisualizer.fit(x_train, y_train) # Fit the training data to the visualizer\nvisualizer.score(x_test, y_test) # Evaluate the model on the test data\ng = visualizer.poof() # Draw/show/poof the data\n\n\n\n","execution_count":28,"outputs":[{"output_type":"stream","text":"[0 0 0 ... 0 0 0]\nK-Nearest Neighbors Accuracy 0.6680809086204594\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/base.py:213: FutureWarning:\n\nFrom version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"**3.Naive Bayes**\n\nNaive-Bayes algorithm calculates the probability of the connection of a feature with a target variable and then it selects the feature with the highest probability."},{"metadata":{"trusted":true},"cell_type":"code","source":"#import the necessary module\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\n#Create an object of GaussianNB\ngnb = GaussianNB()\n#train the algorithm on taining data and predict using the testing data\n#Fitting the data\ngnb.fit(x_train,y_train)\n#prediction \ny_pred = gnb.predict(x_test)\nprint(y_pred)\n\n#print the accuracy score\nprint(\"Naive Bayes Accuracy \",accuracy_score(y_test,y_pred,normalize = True))\n\n#Confusion Matrix\nprint(confusion_matrix(y_test,y_pred))\n\n\n\nfrom yellowbrick.classifier import ClassificationReport\n# Instantiate the classification model and visualizer\nvisualizer = ClassificationReport(gnb, classes=['0','1'])\nvisualizer.fit(x_train, y_train) # Fit the training data to the visualizer\nvisualizer.score(x_test, y_test) # Evaluate the model on the test data\ng = visualizer.poof() # Draw/show/poof the data\n","execution_count":24,"outputs":[{"output_type":"stream","text":"[0 1 0 ... 1 0 1]\nNaive Bayes Accuracy  0.5545774280515492\n[[773747 189017]\n [664826 289338]]\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:143: FutureWarning:\n\nThe sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n\n/opt/conda/lib/python3.7/site-packages/sklearn/base.py:213: FutureWarning:\n\nFrom version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 2 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**4.Decision Tree**\n\nDecision tree algorithm falls under the category of supervised learning. They can be used to solve both regression and classification problems."},{"metadata":{"trusted":true},"cell_type":"code","source":"#import the necessary module\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\n#Create an object of GaussianNB\ndec_tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=5)\n#train the algorithm on taining data and predict using the testing data\n#Fitting the data\ndec_tree.fit(x_train,y_train)\n#prediction \npred = dec_tree.predict(x_test)\nprint(pred)\n\n\n\n\n\n#print the accuracy score\nprint(\"Decision Tree Accuracy \",accuracy_score(y_test,pred,normalize = True))\n\n#Confusion Matrix\nprint(confusion_matrix(y_test,pred))\n\n\n\n\n\nfrom yellowbrick.classifier import ClassificationReport\n# Instantiate the classification model and visualizer\nvisualizer = ClassificationReport(dec_tree, classes=['0','1'])\nvisualizer.fit(x_train, y_train) # Fit the training data to the visualizer\nvisualizer.score(x_test, y_test) # Evaluate the model on the test data\ng = visualizer.poof() # Draw/show/poof the data\n\n\n\n\nfrom sklearn import tree\ntree.plot_tree(dec_tree)","execution_count":25,"outputs":[{"output_type":"stream","text":"[0 1 0 ... 0 1 1]\nDecision Tree Accuracy  0.5911599183693911\n[[781566 181198]\n [602519 351645]]\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/base.py:213: FutureWarning:\n\nFrom version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"[Text(269.7, 274.065, 'X[0] <= 72.0\\nentropy = 1.0\\nsamples = 5750784\\nvalue = [2887476, 2863308]'),\n Text(251.09999999999997, 224.235, 'X[0] <= 52.0\\nentropy = 0.999\\nsamples = 5578801\\nvalue = [2887476, 2691325]'),\n Text(148.79999999999998, 174.405, 'X[2] <= 6.302\\nentropy = 0.998\\nsamples = 5222385\\nvalue = [2764524, 2457861]'),\n Text(74.39999999999999, 124.57499999999999, 'X[3] <= 0.5\\nentropy = 0.995\\nsamples = 4587657\\nvalue = [2488869, 2098788]'),\n Text(37.199999999999996, 74.745, 'X[0] <= 29.0\\nentropy = 1.0\\nsamples = 1644852\\nvalue = [841369, 803483]'),\n Text(18.599999999999998, 24.914999999999964, 'entropy = 0.995\\nsamples = 1556751\\nvalue = [841369, 715382]'),\n Text(55.8, 24.914999999999964, 'entropy = 0.0\\nsamples = 88101\\nvalue = [0, 88101]'),\n Text(111.6, 74.745, 'X[0] <= 8.0\\nentropy = 0.99\\nsamples = 2942805\\nvalue = [1647500, 1295305]'),\n Text(92.99999999999999, 24.914999999999964, 'entropy = 0.994\\nsamples = 393266\\nvalue = [179283, 213983]'),\n 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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"**5.Random Forest**\n\nRandom forest, like its name implies, consists of a large number of individual decision trees that operate as an ensemble. Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model’s prediction "},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\n#Create an object of GaussianNB\nrandom_forest = RandomForestClassifier(criterion=\"entropy\", max_depth=3)\n#train the algorithm on taining data and predict using the testing data\n#Fitting the data\nrandom_forest.fit(x_train,y_train)\n#prediction \npred = random_forest.predict(x_test)\nprint(pred)\n\n\n\n\n\n\n#print the accuracy score\nprint(\"Random Forest Accuracy \",accuracy_score(y_test,pred,normalize = True))\n\n\n\n\n\n#Confusion Matrix\nprint(confusion_matrix(y_test,pred))\n\nfrom yellowbrick.classifier import ClassificationReport\n# Instantiate the classification model and visualizer\nvisualizer = ClassificationReport(random_forest, classes=['0','1'])\nvisualizer.fit(x_train, y_train) # Fit the training data to the visualizer\nvisualizer.score(x_test, y_test) # Evaluate the model on the test data","execution_count":26,"outputs":[{"output_type":"stream","text":"[0 1 0 ... 1 0 1]\nRandom Forest Accuracy  0.5812717013888888\n[[829532 133232]\n [669440 284724]]\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/base.py:213: FutureWarning:\n\nFrom version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n\n","name":"stderr"},{"output_type":"execute_result","execution_count":26,"data":{"text/plain":"0.5812717013888888"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 576x396 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAcgAAAFOCAYAAAACSI0vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3de1hVVeL/8fc5HO4QcpHjBTMElYSsLMcxHDUEcRqnmsnvQJpTNjiVdjNpSi2x0Rjta86U0xVrSmomRr/UVOOEY9lkI6j5Kw0pEVPES9wV4QByOb8/rGPEhqMJmKfP63l8Yu219jprsZ7Oh73PPnub7Ha7HREREWnDfK4HICIi8n2kgBQRETGggBQRETGggBQRETGggBQRETGggBQRETFwWgH52GOPkZSUxA033MD69evb1G3evJkpU6aQlJTEU0895dienp5OUlISycnJ7Ny5s2tHLSIiYqCwsJD4+HheeeWVdnUd5VVHLM4a5OXlsWfPHrKysqiuruYXv/gFEydOdNQvWbKEF154AavVytSpU0lMTKSqqori4mKysrIoKipi3rx5rFmz5gynKSIicvpsNhuLFy9m9OjRhvVGeRUZGdlhf06PIEeOHMkTTzwBQEBAAPX19bS0tABQUlJCQEAAffv2xWw2M27cOHJzc8nNzSU+Ph6AyMhIampqqK2tPePJioiInC4PDw8yMjIIDQ1tV9dRXnXG6RGkm5sbPj4+AKxZs4axY8fi5uYGQHl5OUFBQY62ISEhlJSUUF1dTXR0tGN7cHAw5eXl+Pn5Gb5Ga2srdXV1uLu7YzKZnA1JRES6kd1up6mpCV9fX8zmrr1Upbm52XGQdabc3NywWDqOLYvF0mF9R3nVGacB+bUNGzawdu1aXnzxRcc2o7vUmUymdtvtdnunwVdXV0dhYeHpDkVERHrAkCFD8Pf377L+mpub2fbBf/EIMD5YcsbNzY2YmJhOQ7IjHeVVZ07rVTZt2sSzzz7LqlWr2vyyrFYrFRUVjnJpaSm9e/fGYrG02V5WVkZISEiH/bu7uwMQ+MA83CorT2dIcp7ok7cZAPu+ped4JNLVTOEPAmB/N+Ucj0S6WtNPnqawsNDx3txVWlpa8Ajw47+/eYSGsjN7r/cKDSb2hTRaWlq+U0B2lFedcfoqx48f57HHHuOll16iV69eberCwsKora3l4MGD9OnTh40bN7J8+XKqq6tZuXIlycnJFBQUEBoa2uHpVTiV4m6VlbiVlTkbkpxHPD09AbCbbed4JNLVTF+vbcvRczwS6WomD4+T/+2mj7wayiqpP1LhvGEX6iivOuM0INetW0d1dTX33nuvY9uoUaMYOnQoCQkJLFq0iLlz5wJwzTXXEB4eTnh4ONHR0SQnJ2MymUhLSzvLqYmIiHQuPz+fZcuWcejQISwWCzk5OcTFxREWFtZhXnXGaUAmJSWRlJTUYf3IkSPJyspqtz01NdVZ1yIiIl0mJiaGzMzMDus7yquO6E46IiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBhSQIiIiBiyn06iwsJBZs2Zxyy23cNNNNzm2l5aWkpqa6iiXlJQwd+5cwsPDmTVrFgMHDgRgyJAhPPzww108dBEROR+NBOxnuI+pOwbihNOAtNlsLF68mNGjR7ers1qtZGZmAtDc3Mz06dOJi4tj165dJCYmsmDBgq4fsYiISA9weorVw8ODjIwMQkNDO233+uuvk5iYiK+vL3V1dV02QBERkXPBaUBaLBa8vLycdrRmzRqmTJkCnDzq3L59OykpKUybNo28vLyzH6mIiEgPOq3PIJ35+OOPGTRoEH5+fgBERUUxe/ZsJkyYwL59+5gxYwbr16/Hw8OjK15ORESk23VJQL7//vttPqOMiIggIiICgPDwcEJCQigtLWXAgAFd8XIiIiLdrku+5vHpp58SFRXlKK9du5bVq1cDUF5eTmVlJVartSteSkREpEc4PYLMz89n2bJlHDp0CIvFQk5ODnFxcYSFhZGQkACcDMHg4GDHPgkJCaSmppKTk8OJEydYtGiRTq+KiMh5xWlAxsTEOL7K0ZG33nqrTTkgIICMjIyzG5mIiMg5pDvpiIiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGOiSByaLiIh8H6Snp7Njxw5MJhPz589n+PDhjrpXX32VN998E7PZTExMDAsWLOi0LwWkiIi4hK1bt1JcXExWVhZFRUXMmzePNWvWAFBbW8sLL7zA+vXrsVgs3HrrrXzyySdcdtllHfanU6wiIuIScnNziY+PByAyMpKamhpqa2sBcHd3x93dHZvNRnNzM/X19QQEBHTanwJSRERcQkVFBYGBgY5ycHAw5eXlAHh6ejJ79mzi4+OJi4vjsssuIzw8vNP+FJAiIuIS7HZ7u7LJZAJOnmJ97rnneOedd9iwYQOffPIJn3/+eaf9KSBFRMQlWK1WKioqHOWysjJCQkIA2Lt3LwMGDCAoKAgPDw+uvPJK8vPzO+1PASkiIi4hNjaWnJwcAAoKCggNDcXPzw+A/v37s3fvXhoaGrDb7eTn53PRRRd12p+uYhUREZcwYsQIoqOjSU5OxmQykZaWRnZ2Nv7+/iQkJPCb3/yGX//617i5uXH55Zdz5ZVXdtqfAlJERFxGampqm3JUVJTj5+TkZJKTk0+7L51iFRERMaCAFBERMaBTrCIi0qMGWRtwM9nOaJ+W0AYqnDfrUjqCFBERMaCAPEuesVcR+s46rB+8T8jfXsWtb592bcxWKyF/e5U+27ZgfW8DHqNGAWDy8SHwTyuwfvA+1o3vErDwYTCfWhKfG35Jv8LP8fnlL3psPnLKe7nFXPGLl4hKzGDijCwOfnm8XZs3393D5de9xLCfruInN75KfuHJu3bU1p3g1nnriErMYFDcc7zyj12OfbbsOMyP/yeTiyet4spfvsy6/+ztsTnJSe/tqOCKez8g6o6NTHw4j4MV9R22/ee2UszXvs3+0lNHPHuP1HHFvR+Q8HBem7YJD+cx8NYNXHzHRsc/OX8pIM+CyduboKefojr1d5SOHU/D+x/Q6w/p7doF/WkFDRvf58uRozj68EL8ZtwMgP9dd2Jy96B03NWUTpyEx/Dh+CT96mTd7Fl4T55M8169eZ4LdbYT3HjfW2QsmcTnOTOZOCacWYvWt2lzqPQ4tzy4jlcfn0zBv1K4cfIwbl94ss3ipzdTZ2ui4F8pfPDXqTzwv++zr+QodrudKXe9wcI7r+Kzd1J4aek1TJv7NseON56Laf4g1TU0c+Py/0fGXZfy+TNXM3FEb2Y986lhW1tjC/NWf06Qv7tj2+6Dtfx88TauHNyrXfujdU289rsr+OyZqx3/5Px1WgFZWFhIfHw8r7zySru666+/nunTpzv+lZaWAicfOZKUlERycjI7d+7s2lF/T3iOiaX5wAGavrobQ11mJl7jxmHy9XW0cevXF49LhlP74l8AaPzvZqpunwWAe1QUjbm5YLdDUxONH32Ee9TQk+02b6Zyxq3Ya+t6eFYC8F7eAQYNCGBE9MkzArcnX0bOh/s4XnsqyNwtZv76+M8ZFnnyTh1jrujPrqKTn5Js2Lyfm38Zg9lsIqyPP9fFD+Yf7xZRfayBQ6W1TBg9EICYIb3x8bKw7+DRHp7hD9d7OysZZPVhRMTJG1XfPmkgOR+Xc9zW3K7tor/u5qbx/fH3PnW5hpeHmXeX/JjRUYHt2h+ra6KXry7tcBVOA9Jms7F48WJGjx7dYZvMzEzHP6vV2uaRI0uWLGHx4sVdOujvC8ugQbTsL3aU7TYbrdXVWL5xdwb3YcNoLjlAwPx5WD94n95r1+AeHQ1A44cf4j1pEnh5YfL3x+snP6Hxg00AnPj4kx6di7RVuL+KiAGnjhD8fD0I7uVN0YFTQRYa7MuksYMc5X99sI9Rl/YFwGQy0dJy6r6Qfj4eFB2oJqiXN5cPC+Vvb38GwIcfHcRiMXNxRHB3T0m+Unioloi+Po6yn7eFYH8Pio60/WP00/01bPikgjnXDWqzfWCoD32DvAz7PlrXzMJXdzP8rv/wo/s28eaWL7t+AtJjnAakh4cHGRkZhIaGGtbX1bU/wunskSOuxOTtjb2x7akxe0MDJp9T//OZL7jg5JFi3hZKx47Hlp1N8Krnwc2N2pdeBncL/XZ+Qr8dH9O8fz8N777X09MQA7b6Zjw92x4JeHtaqLM1GbZ/N7eYP738ESvmxQEQf9VFPP3qxzQ0NnPgcA1vbCikofHkEcrziycxd+lGgn/0JAkzsnjyoXg8PXTU0VNsjS14uru12ebt4UZdY4ujbLfbuePpT3nythjcLaf/SdSU2L6kTLyQnSvH8ceUaKav+ISiwzoLdL5yuvIWiwUvL+O/lgCOHj3K3LlzSU5O5o9//CN2u73TR464ErvNhsnTs802k7c3dtup/yFajx+npaKChvUnP5uq++vfMPfqhWXQIAIeWkDLgRIOD4vh8LAYTD4++N1xe4/OQYz5+rjT2Nj2lJutoRk/X/d2bd/YsIcZD67jrWd/6Tjd+vCs0fSz+nHptX/hjrT1TBo7iF4XeFHf0MQv73ydvz9xHZVb72b76zdz28Icig8d65F5Cfh6WWhsammzzdbYgp/XqdB8PucAF1/ox5hhQWfU99N3XELiiJMHE7HDghh/STDrP3G9974firO+SGfOnDk88sgjZGZmUlBQwPr16zt95IgraS4qwhIZ4Sibg4IwBwTQ/MW+U20OlGD29YVvzr+1FVpb8Bo7Ftubb0FzM/aGBurXr8dz9I97cgrSgahBQXy+r8pRLq+yUX2sgcED237utGHzfu599F1yXvwVV17S17Hd18eDF9J/yu6cmfwzYwq1dU1cMqQ3u/ZU0NJid3wGOSwyhMEDA9m680jPTEyICvPl84On/ogtP9ZIdW0Tg/udunbgzS1f8uaWUvr++t/0/fW/Kamo50dzP2Tjzo6/iXeiqZX84po22+x2O+5urvfe90Nx1gE5depU/Pz8cHd3Z/z48ezevbvTR464kobNubj17YvHyJEA+N06g/oN72KvP3XJePPu3TQXF+M79UYAvCf/jNaaGpr3F9O0dy9e8RNONjSb8bp6PM2f7+7xeUh7V4+6kINfHufDjw4CsDJzO5OvjsDXx8PRxlbfxK3z/sX/rby+3WeIy57fwtylJ0+XFxRV8G7ufq6bEMnA/gEcPd7Itq8C8cDhGnYVVTiOPKX7XX1JCAcr6vmw4OQfQCvf3s/kkaH4ep06zf3PtFGUZk7kyOoEjqxOYECIN1sfH8PVwztepxPNrYx9MNfR76f7a9hUUEX8pb27d0LSbc7qg4+qqioeeOABnn76adzd3dm2bRuJiYlYrVZWrlxJcnJyu0eOuJSGBqrumEWv9CWYvX1o3r+fqjn34X7ZZQTcn0rFtJsAqJx5G8HPPI3/7Fm0VFZS+dvboaWFY4sW0esP6Vg//ACApo8/oebJlQCEvPoKbmH9cevfn4BBg/C/526O/WEZDe+8c86m+0Pi7eXOa3+8ljt//2/q6puIvDCQvyz9KVt3HmHhE5t454Vf8Y9391BeZeOm1Lfb7Pv+Kzdyyy9juHHOm0RMeA5vLwsvL/sZvS44+VHF6sd+RsqCd2g80YzZbGLZ/eOJHqyA7Cnenm689rsR3Pnsp9Q1tBDZ15e/3HspWwurWfhqIe88MqrT/Z/9VzFPvPkFx2zN1NiaufiOjfxoSC9ennM5f39gBHc/l0/9iRa8PNx4+d7LCO/j02l/8v1lsn/7fOi35Ofns2zZMg4dOoTFYsFqtRIXF0dYWBgJCQmsWrWKdevW4eHhwbBhw3jooYcwm80sX76cjz76yPHIkW/eUf3bGhsbyc/PJyTlt7iVlXX5JOXcCTtUAoC98IFzPBLpaqYhywCwv/XzczwS6WonJq4lPz+fmJgYPL91ncXZOJv3+pbQUCpWPd/lY+qM0yPImJgYMjMzO6xPSUkhJSWl3fZvP3JERETkfKI76YiIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBhQQIqIiBiwnOsBiIjID0vvS/zwPNZwRvs0BvhR0U3j6YiOIEVERAwoIEVERAwoIEVERAwoIEVERAwoIEVERAwoIEVERAwoIEVERAycVkAWFhYSHx/PK6+80q4uLy+PX/3qVyQnJzNv3jxaW1vJz89n7NixTJ8+nenTp7N48eIuH7iIiEh3cnqjAJvNxuLFixk9erRh/cKFC1m9ejV9+vTh7rvvZtOmTXh7e5OYmMiCBQu6fMAiIiI9wekRpIeHBxkZGYSGhhrWZ2dn06dPHwCCgoKorq6mrq6ua0cpIiLSw5wGpMViwcvLq8N6Pz8/AMrKyti8eTPjxo3DZrOxfft2UlJSmDZtGnl5eV03YhERkR7QJfdirays5Pbbb2fhwoUEBgYSFRXF7NmzmTBhAvv27WPGjBmsX78eDw+Prng5ERGRbnfWAVlbW8vMmTO55557GDNmDAARERFEREQAEB4eTkhICKWlpQwYMOBsX05ERKRHnPXXPJYuXcrNN9/MuHHjHNvWrl3L6tWrASgvL6eyshKr1Xq2LyUiItJjnB5B5ufns2zZMg4dOoTFYiEnJ4e4uDjCwsIYM2YMb7zxBsXFxaxduxaAyZMnM2nSJFJTU8nJyeHEiRMsWrRIp1dFROS84jQgY2JiyMzM7LA+Pz/fcHtGRsZ3H5WIiMh3kJ6ezo4dOzCZTMyfP5/hw4c76o4cOcJ9991HU1MTw4YN4/e//32nfelOOiIi4hK2bt1KcXExWVlZLFmypN1NapYuXcqtt97K2rVrcXNz4/Dhw532p4AUERGXkJubS3x8PACRkZHU1NRQW1sLQGtrK9u3bycuLg6AtLQ0+vXr12l/CkgREXEJFRUVBAYGOsrBwcGUl5cDUFVVhZ+fH08++SQ33XQTjz/+OHa7vdP+FJAiIuISvh14drsdk8nk+Lm0tJQbbriBl19+mYKCAv7zn/902p8CUkREXILVaqWiosJRLisrIyQkBIDAwED69u3LhRdeiJubG6NHj2bPnj2d9qeAFBERlxAbG0tOTg4ABQUFhIaGOm6HarFYGDBgAPv37wdg165dhIeHd9pfl9xqTkRE5FwbMWIE0dHRJCcnYzKZSEtLIzs7G39/fxISEpg/fz5paWk0NjYyePBgxwU7HVFAioiIy0hNTW1TjoqKcvw8cOBAXnrppdPuS6dYRUREDCggRUREDCggRUREDCggRUREDCggRUREDCggRUREDCggRUREDCggRUREDOhGASIi0qNMo3phamw9s308e3XTaDqmI0gREREDCkgREREDCkgREREDCkgREREDCkgREREDCkgREREDCkgREREDCkgREREDCkgREREDCkgREREDCkgREREDCkgREREDpxWQhYWFxMfH88orr7Sr27x5M1OmTCEpKYmnnnrKsT09PZ2kpCSSk5PZuXNn141YRESkBzh9mofNZmPx4sWMHj3asH7JkiW88MILWK1Wpk6dSmJiIlVVVRQXF5OVlUVRURHz5s1jzZo1XT54ERGR7uL0CNLDw4OMjAxCQ0Pb1ZWUlBAQEEDfvn0xm82MGzeO3NxccnNziY+PByAyMpKamhpqa2u7fvQiIiLdxOkRpMViwWIxblZeXk5QUJCjHBISQklJCdXV1URHRzu2BwcHU15ejp+fX6ev1SdvM56enqc7djmPmIYsO9dDkG5i+vlb53oI0tUaG8/1CL4XzuqByXa7vd02k8nUbrvdbsdkMjntr3XVr2htrDqbIcn3jPm+TQC8Zxp6jkciXS3OvhuAg/0HnOORSFfr/UXRuR7C98JZBaTVaqWiosJRLi0tpXfv3lgsljbby8rKCAkJOZuXEhER6VFn9TWPsLAwamtrOXjwIM3NzWzcuJHY2FhiY2PJyckBoKCggNDQUKenV0VERL5PnB5B5ufns2zZMg4dOoTFYiEnJ4e4uDjCwsJISEhg0aJFzJ07F4BrrrmG8PBwwsPDiY6OJjk5GZPJRFpaWrdPREREpCs5DciYmBgyMzM7rB85ciRZWVnttqempp7dyERERM4h3UlHRETEgAJSRETEgAJSRETEgAJSRETEgAJSRETEgAJSRETEgAJSRETEgAJSRETEgAJSRETEgAJSRETEgAJSRETEwFk97kpERORMmcIvwNTSemb7uF3QTaPpmI4gRUREDCggRUREDCggRUREDCggRUREDCggRUREDCggRUREDCggRUREDCggRUREDCggRUREDCggRUTEZaSnp5OUlERycjI7d+40bPP4448zffp0p33pVnMiIuIStm7dSnFxMVlZWRQVFTFv3jzWrFnTpk1RURHbtm3D3d3daX86ghQREZeQm5tLfHw8AJGRkdTU1FBbW9umzdKlS5kzZ85p9aeAFBERl1BRUUFgYKCjHBwcTHl5uaOcnZ3Nj370I/r3739a/SkgRUTEJdjt9nZlk8kEwNGjR8nOzmbGjBmn3Z8CUkREXILVaqWiosJRLisrIyQkBIC8vDyqqqqYNm0ad955J7t27SI9Pb3T/hSQIiLiEmJjY8nJyQGgoKCA0NBQ/Pz8AJg0aRLr1q3j73//O3/+85+Jjo5m/vz5nfZ3Wlexpqens2PHDkwmE/Pnz2f48OEAlJaWkpqa6mhXUlLC3LlzCQ8PZ9asWQwcOBCAIUOG8PDDD5/5bEVERE7TiBEjiI6OJjk5GZPJRFpaGtnZ2fj7+5OQkHDG/TkNyM4um7VarWRmZgLQ3NzM9OnTiYuLY9euXSQmJrJgwYIzHpCIiMh39c2DNoCoqKh2bcLCwhzZ1Rmnp1hP57JZgNdff53ExER8fX2pq6tz+sIiIiLfZ04D0tlls19bs2YNU6ZMAcBms7F9+3ZSUlKYNm0aeXl5XThkERGR7uf0FGtnl81+7eOPP2bQoEGOD0OjoqKYPXs2EyZMYN++fcyYMYP169fj4eHRhUMXERHpPk4DsrPLZr/2/vvvM3r0aEc5IiKCiIgIAMLDwwkJCaG0tJQBAwZ01bhFRES6ldNTrJ1dNvu1Tz/9tM0HoWvXrmX16tUAlJeXU1lZidVq7cpxi4iIdCunR5Cnc9lseXk5wcHBjn0SEhJITU0lJyeHEydOsGjRIp1eFRGR88ppfQ/S2WWzb731VptyQEAAGRkZZzk0ERGRc0d30hERETGggBQRETGggBQRETGggBQRETGggBQRETGggBQRETGggBQRETGggBQRETFwWjcKEBER6TKDIsFsO7N9Wn2gh5+kqCNIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERAwpIERERA5ZzPYDz3Xt7jvK7t/ZRd6KVAb08eTF5MGG9PNu08bz/v0QEeznKIy/04+WpQ3llexl3Ze+lr7+Ho+7Rnw3kF5eE8OmROu7O3kvp8SYsbibSEi/khuEhPTYvgcCrf0zk8t9h9vWh8cBhPpsxj8ZDpW3a9L3ll1x4/2/AZKLx4Jfsnv176vfsByDk+ngiH7sfs7cXNVt28NmMebQcr8MnahBDn1mEhzUEe3Mz+9JWUv76v8/BDH+4PGOvIuDhhzD5+NBy6BDV982l5ciXbdqYrVaC/rQCS2Qk9uPHqZ63gBNbthC08knch19yqp2/P40fbafqt7e12bfP++9xNG0Rtr+v6bF5SddSQJ6FusYWpr6ym3UzoxkR5sfjGw8y6/+KePM30Y42tY0tmE1Q8OAV7fY/Vt9M0mW9efZ/ItvV/erlz/nDzy7i+kuC+fhgLeOe2snVkQEE+bh365zkJLOPN9GvreCTSSnUflzAgLm3MvSZRey89g5HG5+hg4j439+x9dJrOXG4jH63JXPxi+n8v59MxeuiMIY+ncb/+8k06r8oYfCKeYRMvprSv71NzJonKHn8Lxx5KRvfmCFckfsaVf1zaampPYcz/uEweXsT9PRTVEybTlN+Pn633UavP6RTecutbdoF/WkFDRvfp/bGaXjGXoXfjJup2rKFqrvubtMuePXL2Na0DcFev19E67Fj3T4X6V6nFZDp6ens2LEDk8nE/PnzGT58uKPu+uuvx9/f31Fevnw5Vqu1031cxXtFRxkU5MWIMD8AbruqLwv+Vczxhmb8vU7+ao81NNPL2/jXfKyhhV7ebu22N7W0kpZ4IdfFBAFweZgfXhYzxVWNCsgeEhj3Y+q/KKH24wIADj/7GhHpc3Dz86Wltg4A32ER1O/Zz4nDZQBUv5dHxNK5APS56VrK/2899XsPALBnTvrJjs1m9i95hvK1OQDU5RdibzyBd3gYtTs+78kp/mB5joml+cABmvLzAajLzCTgwd9h8vXFXndybd369cXjkuFUTL8ZgMb/bqbxv5vb9eV19XhMnh40/HtD223ePjTm5vXAbKQ7OQ3IrVu3UlxcTFZWFkVFRcybN4813/prKTMz84z3cQV7yusZ9I1Tp36ebgT7WCiqaODyr0LzaH0zLa12rn1hF0XlDQwM8mTFdYO42OrD0fpm8vYf56ondnC0vplrhgXy6DUX4Wkxk3x5b0e/b3xaSaCPhWF9fHp8jj9UPkMuon5viaPcUmejqfIo3pEXUvvJZwAcy9uBd8SF+EYPpm7XHkJvmEj1v0++ifpdOpSGA0e4bP2LeA3sRw+wMCcAABDXSURBVPV7W9hz3x9orW+gLGudo98LfjQcTCZshft7dH4/ZJZBg2jZX+wo2202WqursVx0EU27dgHgPmwYzSUHCJg/D6/4CbSWlXM0bZGj/msXzJ3L0SWPOsomLy8CHn6Iiltu5YI59/bMhKTbOL1IJzc3l/j4eAAiIyOpqamhtvbUqaC6r/7iOpN9XIWtqRUv97a/Qm93M3UnWhxlH3c3plwawuPXDiL/dyNIHBrIL14soLnFzvB+vvxsWCDvzorhv3dfyrYDtTz23kHHvrn7a7jw91u5K3svq5IG42nRNVU9xc3Hm9aGxjbbWusbcfM99UfKiSNl7J2/gpGfvMFPKrfQf/Y09j64HABLrwsISriKXdNS2Xr5L/COGMBF829v059nWB+i//o4hXctobW+ofsnJcDJU6z2xrZra29owORzam3NF1yAe1QUjXlbKB07Hlt2NsGrnge3U2d8PK8aDSYTJ/JOHSn6z7kX2xv/oOXAge6fiHQ7p++4FRUVBAYGOsrBwcGUl5c7ykePHmXu3LkkJyfzxz/+Ebvd7nQfV+Hr4UZDU2ubbbamVvw8T/1PFB7sxdNTIhnc2xuz2cQ9Y/tRWttEYXk9N10RygMTBuDt7kagj4V7xvbjnwVVjn1HX3QBBxb+iLdShjE1czc7DrveHxnfVy11NsxebS+2Mvt4OU6vAvhddjEXLbiD3EHxbAoexd4HH+eSN58BoPnYccrfeJem8ipabfUceuZvBE2MdezrMyScEe9nsv8Pz1H617d6ZlICnDxiNHm2XVuTtzd226m1bT1+nJaKChrWrweg7q9/w9yrF5ZBgxxtvK+/Hts//uEoW4YOxevq8Rx/5tlunoH0FKcBabfb25VNJpOjPGfOHB555BEyMzMpKChg/fr1TvdxFUNDvdldbnOUy2ubqLY1MzjE27HtaH0zX1SeOjowmUy02sHdzcQXlQ0cq2921Nk5ub3K1sSr28sc2y/r78eogf68X6QP/XuK7fMv8IkKd5TdQwJxDwzAtufUqbnACaM5tvljGkuOAFCWtQ6/6MG4hwTSsO8glgA/R1t7Syv2lpNnFjz6hXJpziqKHnycIy+s7aEZydeai4qwREY4yuagIMwBATR/se9UmwMlmH194ZvvW62t0Hrq7JD3hAk0vPveqXJCPG79+tF3ax59P96Oz89/Tq9HFuF/913dOyHpNk4D0mq1UlFR4SiXlZUREnLq6wZTp07Fz88Pd3d3xo8fz+7du53u4yqujgzg4NETfPjFyeD684eH+dmwIHy/cQS583AdcU9/SunxEwBk5H1JWIAHg4K9ePTfB3jwn/ux2+00NLXy3OYjXHNxEO5mE3e/vpf39hwFoOz4CbYeOM4lfX17fpI/UNUbt+AV1oeA2JNXH4fdNZ2KtzfSaqt3tLHt3kfAVZdjCeoFQPDPxtF4pIymimq+zPwHob/6KZ79rWA20/fWG6jakAvA0GceoeSPL1O+9p2en5jQsDkXt7598Rg5EgC/W2dQv+Fd7PWn1rZ5926ai4vxnXojAN6Tf0ZrTQ3NX312aQ4OxhwSTPMXXzj2Of7npzgSM5wjl1/BkcuvwPbWWxxNW8TxJ1f24OykKzm9SCc2NpaVK1eSnJxMQUEBoaGh+Pmd/Mu4qqqKBx54gKeffhp3d3e2bdtGYmIiVqu1w31cibe7G3+bPpS7svdSd6KViBAv/pI8hK0HjrPwX8W8c1sMYyMCmDOuH+P+vBOzyUS/AA/W3nIxbmYT/3ttOL/9exFRS7djNpn46cWB3De+P54WM2tvuZgH395PbWMLLa127hzTj7jBvc71lH8wWhsayU+aw5CnFuLm60190QE+u+VB/EdewqDF97BjUgqVb2/kyyuiuTIvC3urnZaaWvL/5+SFGbU7d7P3d//LiA//ir2pmaObtlO89Hk8+obS+9o4fKPC6X/HjY7XK7r/MSrf3niupvvD0tBA1R2z6JW+BLO3D83791M15z7cL7uMgPtTqZh2EwCVM28j+Jmn8Z89i5bKSip/ezt8dRbArW9fWisr4Vtny8S1mOzfPh9qYPny5Xz00UeYTCbS0tIoKCjA39+fhIQEVq1axbp16/Dw8GDYsGE89NBDmM3mdvtERUV12H9jYyP5+fkMy/s9no1VHbaT84/5vk0AvGcaeo5HIl0tzr4bgIP9B5zjkUhX6/1FEfn5+cTExOD5rc9rz8bX7/XRvm/jabY53+Gb+7b6sKtucpePqTOn9T3I1NTUNuVvhl1KSgopKSlO9xERETmf6HsDIiIiBhSQIiIiBhSQIiIiBhSQIiIiBvQ0DxER6VGmwMGY3JvObJ8md2h/Z9NupSNIERERAzqCFBERl9HZoxbz8vJYsWIFZrOZ8PBwHn30Uczmjo8TdQQpIiIu4ZuPWlyyZAmLFy9uU79w4UKefPJJXnvtNerq6ti0aVOn/SkgRUTEJTh71GJ2djZ9+vQBICgoiOrq6k77U0CKiIhLcPaoxa/vCV5WVsbmzZsZN25cp/0pIEVExCWczqMWKysruf3221m4cGGbMDWigBQREZfg7FGLtbW1zJw5k3vuuYcxY8Y47U8BKSIiLiE2NpacnBwAw0ctLl26lJtvvtnpqdWv6WseIiLiEkaMGEF0dDTJycmORy1mZ2fj7+/PmDFjeOONNyguLmbt2rUATJ48maSkpA77U0CKiIjL6OzxjPn5+WfUl06xioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGFBAioiIGDitx12lp6ezY8cOTCYT8+fPZ/jw4Y66vLw8VqxYgdlsJjw8nEcffZSCggJmzZrFwIEDARgyZAgPP/xw98xARESkGzgNyK1bt1JcXExWVhZFRUXMmzePNWvWOOoXLlzI6tWr6dOnD3fffTebNm3C29ubxMREFixY0K2DFxER6S5OAzI3N5f4+HgAIiMjqampoba2Fj8/PwCys7MdPwcFBVFdXU1ra2s3DllERKT7Of0MsqKigsDAQEc5ODiY8vJyR/nrcCwrK2Pz5s2MGzcOm83G9u3bSUlJYdq0aeTl5XXD0EVERLqP0yNIu93ermwymdpsq6ys5Pbbb2fhwoUEBgYSFRXF7NmzmTBhAvv27WPGjBmsX78eDw+Prh29iIhIN3EakFarlYqKCke5rKyMkJAQR7m2tpaZM2dyzz33MGbMGAAiIiKIiIgAIDw8nJCQEEpLSxkwYEBXj19ERKRbOD3FGhsbS05ODgAFBQWEhoY6TqsCLF26lJtvvplx48Y5tq1du5bVq1cDUF5eTmVlJVartavHLiIi0m2cHkGOGDGC6OhokpOTMZlMpKWlkZ2djb+/P2PGjOGNN96guLiYtWvXAjB58mQmTZpEamoqOTk5nDhxgkWLFun0qoiInFdO63uQqampbcpRUVGOn/Pz8w33ycjIOIthiYiIy+o1FDzPcJ9G4GB3DKZjupOOiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIAQWkiIiIgdMKyPT0dJKSkkhOTmbnzp1t6jZv3syUKVNISkriqaeeOq19REREusN3yauOWJw12Lp1K8XFxWRlZVFUVMS8efNYs2aNo37JkiW88MILWK1Wpk6dSmJiIlVVVZ3uIyIi0tW+S15FRkZ22J/TI8jc3Fzi4+MBiIyMpKamhtraWgBKSkoICAigb9++mM1mxo0bR25ubqf7iIiIdIfvkledcXoEWVFRQXR0tKMcHBxMeXk5fn5+lJeXExQU5KgLCQmhpKSE6urqDvcxYrfbAWj+9SuYPTycDUnOJ42NAMQ26DS7q2n8am17f1F0jkciXe3EiRPAqffmrtbU1D37fJe86ozTgPz2L8hut2MymQzrAEwmU6f7GGn6auaFhYXOhiMiIj2kqakJLy+vLuvPzc0NNzc3du9uOav9O/Jd8qozTgPSarVSUVHhKJeVlRESEmJYV1paSu/evbFYLB3uY8TX15chQ4bg7u7udMAiItK97HY7TU1N+Pr6dmm/FouFmJgYWlq+e0BaLB3H1nfJq07H62xAsbGxrFy5kuTkZAoKCggNDXWcKg0LC6O2tpaDBw/Sp08fNm7cyPLly6muru5wHyNmsxl/f39nQxERkR7SlUeO32SxWDoNubPxXfKqMyb7aZxkXr58OR999BEmk4m0tDQKCgrw9/cnISGBbdu2OV5k4sSJ/OY3vzHcJyoq6mznLiIi0qnvklcdOa2AFBER+aHRnXREREQMKCBFREQMdM8npZ1IT09nx44dmEwm5s+fz/Dhwx11mzdvZsWKFbi5uTF27Fhmz57d08Prcp3N9/rrr29zcdLy5cuxWq3nYphdprCwkFmzZnHLLbdw0003talztfXtbK6utraPPfYY27dvp7m5mdtuu42JEyc66lxtXaHz+brS2tbX1/Pggw9SWVlJY2Mjs2bN4uqrr3bUu+LanhF7D9qyZYv9t7/9rd1ut9v37NljnzJlSpv6n/70p/bDhw/bW1pa7ElJSfY9e/b05PC6nLP5XnfddediWN2mrq7OftNNN9kfeughe2ZmZrt6V1pfZ3N1pbXNzc21p6Sk2O12u72qqso+bty4NvWutK52u/P5utLa/vOf/7Q///zzdrvdbj948KB94sSJbepdbW3PVI+eYu3q2wB93zm75V5dXd25Glq38PDwICMjg9DQ0HZ1rra+nc0VXGttR44cyRNPPAFAQEAA9fX1ju+xudq6QufzBdda22uuuYaZM2cCcOTIkTZHwq64tmeqR0+xdvVtgL7vOpsvwNGjR5k7dy6HDh1i1KhR3Hvvvef1jRI6+36Tq62vs+9yudLaurm54ePjA8CaNWsYO3as424mrrau0Pl8wbXW9mvJycl8+eWXPPvss45trri2Z6pHA9LexbcB+r7rbL4Ac+bM4dprr8XT05NZs2axfv16EhMTe3qYPcIV17czrri2GzZsYO3atbz44ouOba68rkbzBddc29dee43PPvuM+++/nzfffNPwlqHgOmt7unr0FGtX3wbo+66z+QJMnToVPz8/3N3dGT9+PLt37z4Xw+wRrri+nXG1td20aRPPPvssGRkZbS5QcdV17Wi+4Fprm5+fz5EjRwC4+OKLaWlpoaqqCnDdtT0TPRqQsbGx5OTkAHR6G6Dm5mY2btxIbGxsTw6vy3U236qqKmbOnOm4Ufu2bdsYPHjwORtrd3PF9e2Iq63t8ePHeeyxx3juuefo1atXmzpXXNfO5utqa/vRRx85jpArKiqw2WwEBgYCrrm2Z6rH76TTlbcBOh90Nt9Vq1axbt06PDw8GDZsGA899BBm8/n71dT8/HyWLVvGoUOHsFgsWK1W4uLiCAsLc7n1dTZXV1rbrKwsVq5cSXh4uGPbqFGjGDp0qMutKzifryutbUNDAwsWLODIkSM0NDRw5513cvToUZd+Tz4TutWciIiIgfPzzx4REZFupoAUERExoIAUERExoIAUERExoIAUERExoIAUERExoIAUERExoIAUEREx8P8BTMlL6N2wnywAAAAASUVORK5CYII=\n"},"metadata":{}}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 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diff --git a/intern-basics/EEG_Alcohol/EEG_Alcohol.md b/intern-basics/EEG_Alcohol/EEG_Alcohol.md
new file mode 100644
index 0000000..2575bb1
--- /dev/null
+++ b/intern-basics/EEG_Alcohol/EEG_Alcohol.md
@@ -0,0 +1,51 @@
+# EEG-Alcohol
+
+# Data Set Information:
+This data arises from a large study to examine EEG correlates of genetic predisposition to alcoholism. It contains measurements from 64 electrodes placed on subject's scalps which were sampled at 256 Hz (3.9-msec epoch) for 1 second.
+
+There were two groups of subjects: alcoholic and control. Each subject was exposed to either a single stimulus (S1) or to two stimuli (S1 and S2) which were pictures of objects chosen from the 1980 Snodgrass and Vanderwart picture set. When two stimuli were shown, they were presented in either a matched condition where S1 was identical to S2 or in a non-matched condition where S1 differed from S2.
+
+# Attribute Information
+
+Each trial is stored in its own file and will appear in the following format.
+
+trial number sensor position sample num sensor value subject identifier matching condition channel name time
+
+0 FP1 0 -8.921 a S1 obj 0 co2a0000364 0
+
+0 AF8 87 4.14 a S1 obj 33 co2a0000364 0.33
+
+The columns of data are:
+
+**the trial number**,
+
+**sensor position**,
+
+**sample number** (0-255),
+
+**sensor value** (in micro volts),
+
+**subject identifier**(Alcoholic(a) or Control (c)),
+
+**matching condition**(a single object shown (S1 obj), object 2 shown in a matching condition (S2 match), and object 2 shown in non matching condition (S2 nomatch)),
+
+**channel number**(0-63),
+
+**name**(a serial code assigned to each subject),
+
+**time**(inverse of sample num measured in seconds))
+
+# Working
+**Subject Identifer** is our label of the data, the dataset was divided into 75-25 ratio using train_test split . 
+I used 5 algorithms on my dataset and compared their accuracy:-
+- **Logistic Regression** 55.78%
+- **Naive Bayes** 55.46%
+- **K-Nearest Neighbors**  66.81%
+- **Decision Tree** 59.11%
+- **Random Forest** 58.12%
+
+We further used Confusion Matrix to evaluate performance of our algorithms.
+Since K-Nearest Neighbors has the highest accuracy,we will use K-Nearest Neighbors on our data.
+
+
+# Dataset Link - https://www.kaggle.com/nnair25/Alcoholics