<jupyter_start><jupyter_text>data=pd.read_csv("diabetes.csv")data.sample(5)<jupyter_code>data.info()
data.shape<jupyter_output><empty_output><jupyter_text>We have 768 Rows and 9 Column<jupyter_code>sns.heatmap(data.isnull(),yticklabels=False,cbar=False)
data.isnull().sum()<jupyter_output><empty_output><jupyter_text>It's Look Like We Don't Have Any Missing Value..<jupyter_code>print("Total Number of 0 Present in Output",data[data.Outcome==0].shape[0])
print("Total Number of 1 Present in Output",data[data.Outcome==1].shape[0])
data.columns<jupyter_output><empty_output><jupyter_text>We Have 9 columns form this our target variable is "Outcome" and rest are input for the model<jupyter_code>data.describe().T
df=data.corr()
df<jupyter_output><empty_output><jupyter_text>![download.png](attachment:download.png)<jupyter_code>plt.figure(figsize=(10,10))
sns.heatmap(df,annot=True,cmap="RdYlGn")
plt.show()<jupyter_output><empty_output><jupyter_text>Summary Of HeatMapi.>  OutComes is Highly Correlated Glucose And Averagely Correlated With BMI.ii.> OutComes is less Correlated With BloodPressure And SkinThickness<jupyter_code>#Swarm Plot
sns.swarmplot(x=data['Outcome'],y=data['BMI'])<jupyter_output><empty_output><jupyter_text>Swarm Plot Shows us that:-i.> Most of the person BMI in a data set lies between 22-40ii.> Person Whose BMI is greater than 42 is Having More Chances Of Diabetes.<jupyter_code>#KDE Plot
sns.kdeplot(x=data['Age'],shade=True)<jupyter_output><empty_output><jupyter_text>Summary Of KDE Ploti.> Most Of The People In our Data-set Are Having the Age Between 18-40  Intially it's look like there is no NAN value but ,There are lot of 0 in a data-Set which is actually NaN Value in a Data-Set.. Let's Findout NAN(0) Value In a Data-Set<jupyter_code>new_data=data[['Glucose','BloodPressure','SkinThickness','Insulin','BMI',
           'DiabetesPedigreeFunction','Age']]
Dict_For_Zero_Value=dict()
for i in new_data.columns:
    Dict_For_Zero_Value[i]=new_data[new_data[i]==0].shape[0]
Dict_For_Zero_Value<jupyter_output><empty_output><jupyter_text>We,Have So many 0 values in BloodPressure,SkinThickness and Insulin. Let's Fill out These value With median Value.<jupyter_code>data[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']]=data[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']].replace(to_replace=0,
                                                    value=np.nan)
data.isnull().sum()
data['Glucose'].fillna(data.Glucose.median(),inplace=True)
data['BloodPressure'].fillna(data.BloodPressure.median(),inplace=True)
data['SkinThickness'].fillna(data.SkinThickness.median(),inplace=True)
data['Insulin'].fillna(data.Insulin.median(),inplace=True)
data['BMI'].fillna(data.BMI.median(),inplace=True)
data.isnull().sum()
#Let's Save the Updated Data
data.to_csv("After_EDA.csv")<jupyter_output><empty_output>
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