diff --git a/intern-basics/EEG_Alcohol/EEG_ALCOHOLIC_AJAY.ipynb b/intern-basics/EEG_Alcohol/EEG_ALCOHOLIC_AJAY.ipynb
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+{"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":" \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":"
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FP1
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c
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0.003906
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1.383
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co2c0000337
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155
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6.510
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0.605469
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71
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100000 rows × 10 columns
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"},"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":"
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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":"
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\n \n
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trial number
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sample num
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sensor value
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subject identifier
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matching condition
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channel
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time
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0
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2.360
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c
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S1 obj
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1
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34
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1
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2.848
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c
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S1 obj
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2
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34
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2
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1.383
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S1 obj
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0
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0.007812
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3
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34
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3
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c
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S1 obj
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0
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0.011719
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\n
\n
4
\n
34
\n
4
\n
-3.499
\n
c
\n
S1 obj
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0
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0.015625
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"},"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":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
"},"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":"","image/png":"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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":"","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":"","image/png":"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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":"","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":"","image/png":"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\n"},"metadata":{}}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 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\ No newline at end of file
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