import pandas as pd
df = pd.read_csv('heart.csv')
df.isnull().sum()
df= df.fillna(df.median())
df=df.astype({'oldpeak':'int','thalach':'int'})
X = df.drop('target',axis='columns')
Y = df.iloc[:,-1]
import numpy as np
Y_train = np.ravel(Y_train)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
reg = LogisticRegression()
reg.fit(X_train, Y_train)
Y_pred = reg.predict(X_test)
Y_pred
print(accuracy_score(Y_test,Y_test))
print(classification_report(Y_test,Y_pred))
print(confusion_matrix(Y_test,Y_pred))
sns.heatmap(confusion_matrix(Y_test,Y_pred),annot=True)
x = df['age']
y = df['chol']
plt.scatter(x,y)
plt.show()
plt.bar(x,y)
plt.show()
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import pandas as pd
import numpy as np f
rom sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
df=pd.read_csv("Heart.csv")
print(df)
print(df.to_string)
display(df)
print(df.shape)
print(df.dtypes)
df.astype({'age':'float'})
df.dropna(inplace=True)
x=df["restecg"].mean()
df["restecg"].fillna(x, inplace=True)
print(df)
df.drop_duplicates(inplace=True) df.dropna()
X=df.drop('target',axis='columns') Y=df['target']
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2)
X_train.shape
Y_train.shape
Reg=LogisticRegression()
Reg.fit(X_train,Y_train)
LogisticRegression()
ypred=Reg.predict(X_test)
ypred
print(accuracy_score(Y_test,ypred))
print(classification_report(Y_test,ypred))
print(confusion_matrix(Y_test,ypred))
sns.heatmap(confusion_matrix(Y_test,ypred),annot=True)
X1=['age']
Y1=['target']
plt.scatter(X1,Y1)
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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("D:\heart.csv")
df
df.isna()
df.isnull().sum()
a = df.duplicated().sum()
a
df1 = df.fillna(df.median())
df1
print(df1.to_string())
df1.drop_duplicates()
df = df1.astype({"chol":"int","trestbps":"int","oldpeak":"int"})
df
X = df.drop("target", axis="columns")
X
Y = df["target"]
Y
X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size = 0.25)
X_train.shape
Y_train.shape
X_test.shape
Y_test.shape
Reg = LogisticRegression()
Reg.fit(X_train, Y_train)
Y_predict = Reg.predict(X_test)
Y_predict.shape
print(accuracy_score(Y_test, Y_predict))
print(classification_report(Y_test, Y_predict))
print(confusion_matrix(Y_test, Y_predict))
sns.heatmap(confusion_matrix(Y_test, Y_predict),annot = True)
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import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
df = pd.read_csv("D:\weight-height (1)(1).csv")
df
X = df.iloc[:,1:2]
Y = df.iloc[:,2]
X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size = 0.2)
Reg = LinearRegression()
Reg.fit(X_train,Y_train)
Y_predict = Reg.predict(X_test)
print(Reg.coef_)
print(Reg.intercept_)
plt.scatter(X_test,Y_test)
plt.plot(X_test,Y_predict,color = "red")
print('meansqaureerror',metrics.mean_squared_error(Y_test,Y_predict))
print("meanabsoluteerror",metrics.mean_absolute_error(Y_test,Y_predict))
Rsquare = Reg.score(X_train,Y_train)
print(Rsquare)
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import pandas as pd
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
pip install graphviz
df=pd.read_csv("D:\Admission_Predict .csv",sep=',')
df.columns
df.shape
df.columns = df.columns.str.rstrip()
df.columns
df = df.isnull().sum()
df.loc[df['Chance of Admit'] >=0.80,'Chance of Admit']=1
df.loc[df['Chance of Admit'] <0.80,'Chance of Admit']=0
df['Chance of Admit']
df
df=df.drop('Serial No.',axis=1)
df
X=df.iloc[:,0:7].values
Y=df.iloc[:,7].values
Y
from sklearn.model_selection import train_test_split,StratifiedKFold,cross_val_score
X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size = 0.25,random_state=0)
print(X_train.shape,end=' ')
print(X_test.shape)
model = DecisionTreeClassifier(criterion='entropy', max_depth=2)
model.fit(X_train,Y_train)
Y_Pred=model.predict(X_test)
matrix=confusion_matrix(Y_test,Y_Pred,labels=[0.0,1.0])
matrix
acc=accuracy_score(Y_test,Y_Pred)
print('Accuracy of Decision Tree Model = ',acc)
from sklearn.metrics import classification_report
cr=classification_report(Y_test,Y_Pred)
print('Classification Report ',cr)
feature_names=df.columns[0:7]
print(feature_names,end=' ')
class_names=[str(x) for x in model.classes_]
class_names
from sklearn.tree import plot_tree
fig=plt.figure(figsize=(50,30))
plot_tree(model,feature_names=feature_names,class_names=class_names,filled=True)
plt.savefig('tree_visualization.png')
import graphviz
from sklearn import tree
sf=StratifiedKFold(n_splits=5,shuffle=True,random_state=0)
depth=[1,2,3,4,5,6,7,8,9,10]
for d in depth:
score=cross_val_score(tree.DecisionTreeClassifier(criterion='entropy',max_depth=d,random_state=0),X_train,Y_train,cv=sf,scoring='accuracy')
print("Average score for depth {} is {} :".format(d,score.mean()))
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import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
df=pd.read_csv('pima-indians-diabetes.csv')
df.info
df.columns
X=df.iloc[:,0:-1].values
y=df.iloc[:,8].values
model=Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X,y,epochs=150, batch_size=10)
_, accuracy =model.evaluate(X,y)
print('Accuracy: %.2f' % (accuracy*100))
from ann_visualizer.visualize import ann_viz;
ann_viz(model, title="My First Neural Network")
score.mean()
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// Find and modify a document and return the updated one
const updatedDocument = db.posts.findAndModify({
query: { title: "Updated Document" },
update: { $inc: { likes: 10 } },
new: true, // Return the modified document
});
db.createCollection(“Teacher_info")
db.createCollection("audit", {capped:true, size:20480})
db.Teacher_info.insert( { Teacher_id: “Pic001", Teacher_Name: “Ravi",Dept_Name:
“IT”, Sal:30000, status: "A" }
“IT”, Sal:30000, status: "A" }
db.Teacher_info.insert( { Teacher_id: “Pic002", Teacher_Name: “Ravi",Dept_Name:
“IT”, Sal:20000, status: "A" } )
“IT”, Sal:20000, status: "A" } )
db.Teacher_info.insert( { Teacher_id: “Pic003", Teacher_Name: “Akshay",Dept_Name:
“Comp”, Sal:25000, status: “N" } )
“Comp”, Sal:25000, status: “N" } )
db. Teacher_info.update( { sal: { $gt: 25000 } }, { $set: { Dept_name: “ETC" } }, {
multi: true } )
db. Teacher_info.update( { status: "A" } , { $inc: { sal: 10000 } }, { multi: true } )
multi: true } )
db. Teacher_info.update( { status: "A" } , { $inc: { sal: 10000 } }, { multi: true } )
db.Teacher_info.remove({Teacher_id: "pic001"});
db. Teacher_info.remove({})
db. Teacher_info.remove({})
db.Teacher_info.update( { }, { $set: { join_date: new Date() } }, { multi: true} )
db.Teacher_info.drop()
db.Teacher.find()
db.Teacher_info.find({sal: 25000})
db.Teacher_info.find( { $or: [ { status: "A" } , { sal:50000 } ] } )
db. Teacher_info.find( { sal: { $gt: 40000 } } )
db.media.find( { Released : {$gt : 2000} }, { "Cast" : 0 } )
>db.media.find ( { Released : {$gte : 1999 } }, { "Cast" : 0 }
.Exclude One Field from a Result Set
>db.records.Find( { "user_id": { $lt: 42} }, { history: 0} )
>db.records.Find( { "user_id": { $lt: 42} }, { history: 0} )
.Return Two fields and the _id Field
>db.records.find( { "user_id": { $lt: 42} }, { "name": 1, "email": 1} )
>db.records.find( { "user_id": { $lt: 42} }, { "name": 1, "email": 1} )
.Return Two Fields and Exclude _id
>db.records.find( { "user_id": { $lt: 42} }, { "_id": 0, "name": 1 , "email": 1
} )
>db.records.find( { "user_id": { $lt: 42} }, { "_id": 0, "name": 1 , "email": 1
} )
db..find().pretty()
>db. Teacher_info.find( { status: "A" } ).sort( {sal: -1 } )
>db.audit.find().sort( { $natural: -1 } ).limit ( 10 )
db.Employee.find().sort({_id:-1})
db.Employee.find().sort({_id:1})
>db.Employee.find().skip(3).pretty()
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var mapFunction = function() {
emit(this.product, this.quantity * this.price);
};
var reduceFunction = function(key, values) { return Array.sum(values); };
db.sales.mapReduce( mapFunction, reduceFunction, { out: "product_total_revenue" // Store the results in a new collection } )
db.sales.aggregate([ { $group: { _id: "$product", { $avg: "$price" } } }]);
