title: ValueError: could not convert string to float: 'Q'
body: <p>I am new to programming and I was working with the titanic dataset from Kaggle. I have been trying to build the Logistic Regression model after performing one-hot encoding. But I keep getting the error. I think the error is caused due to the dummy variable. Below is my code.</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  import matplotlib as plt
  import seaborn as sns
  
  #Loading data
  df=pd.read_csv(r"C:\Users\Downloads\train.csv")
  
  #Deleting unwanted columns
  df.drop(["PassengerId","Name","Cabin","Ticket"],axis=1,inplace=True)
  
  #COunt of Missing values in each column
  print(df.isnull().sum())
  
  #Deleting rows with missing values based on column name
  df.dropna(subset=['Embarked','Age'],inplace=True)
  print(df.isnull().sum())
  
  #One hot encoding for categorical variables
  #Creating dummy variables for Sex column
  dummies = pd.get_dummies(df.Sex)
  dummies2=pd.get_dummies(df.Embarked)
  
  #Appending the dummies dataframe with original dataframe
  new_df= pd.concat([df,dummies,dummies2],axis='columns')
  
  print(type(new_df))
  #print(new_df.head(10))
  
  #Drop the original sex,Embarked column and one of the dummy column for bth variables
  new_df.drop(['Sex','Embarked'],axis='columns',inplace=True)
  print(new_df.head(10))
  
  new_df.info()
  
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import classification_report
  from sklearn.metrics import confusion_matrix,accuracy_score
  
  x = df.drop('Survived', axis=1)
  y = df['Survived']
  
  logmodel = LogisticRegression()
  
  logmodel.fit(x, y)
  
  </code></pre>