Q- what is the goal 

“The Titanic challenge hosted by Kaggle is a competition in which the goal is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat. “

Q- this my first scripts : 

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


path = "data/train.csv"
data = pd.read_csv(path)

data.shape
(891, 12)
data.head()

(891, 12)


PassengerId
Survived
Pclass
Name
Sex
Age
SibSp
Parch
Ticket
Fare
Cabin
Embarked
0
1
0
3
Braund, Mr. Owen Harris
male
22.0
1
0
A/5 21171
7.2500
NaN
S
1
2
1
1
Cumings, Mrs. John Bradley (Florence Briggs Th...
female
38.0
1
0
PC 17599
71.2833
C85
C
2
3
1
3
Heikkinen, Miss. Laina
female
26.0
0
0
STON/O2. 3101282
7.9250
NaN
S
3
4
1
1
Futrelle, Mrs. Jacques Heath (Lily May Peel)
female
35.0
1
0
113803
53.1000
C123
S
4
5
0
3
Allen, Mr. William Henry
male
35.0
0
0
373450
8.0500
NaN
S

Titanic Survival Prediction Project
Q: What is the goal?
The goal of the Kaggle Titanic challenge is to predict whether a passenger on the Titanic survived or perished based on features such as age, sex, and passenger class.
Q: My first script:
     import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

path = "data/train.csv"
data = pd.read_csv(path)

print(data.shape)  # Output: (891, 12)
print(data.head()) # Output: (Displays the first few rows of the DataFrame)
   
content_copy Use code with caution.Python
Explanation of the script:
This script begins by importing necessary libraries: pandas for data manipulation, NumPy for numerical operations, matplotlib and seaborn for data visualization. It then reads the Titanic training data from a CSV file located at "data/train.csv" into a pandas DataFrame called data. Finally, it prints the shape (number of rows and columns) and the first few rows of the DataFrame to get a quick overview of the data. The output shows there are 891 rows and 12 columns in the dataset. The data.head() function displays a sample of the data's columns and their values.

Q- describe the data set oky 
The Survived column is the ** target variable**. If Suvival = 1 the passenger survived, otherwise he's dead. The is the variable we're going to predict.
The other variables describe the passengers. They are the features.
PassengerId: and id given to each traveler on the boat
Pclass: the passenger class. It has three possible values: 1,2,3 (first, second and third class)
The Name of the passeger
The Sex
The Age
SibSp: number of siblings and spouses traveling with the passenger
Parch: number of parents and children traveling with the passenger
The ticket number
The ticket Fare
The cabin number
The embarkation. This describe three possible areas of the Titanic from which the people embark. Three possible values S,C,Q
data.describe()



PassengerId
Survived
Pclass
Age
SibSp
Parch
Fare


count
891.000000
891.000000
891.000000
714.000000
891.000000
891.000000
891.000000
mean
446.000000
0.383838
2.308642
29.699118
0.523008
0.381594
32.204208
std
257.353842
0.486592
0.836071
14.526497
1.102743
0.806057
49.693429
min
1.000000
0.000000
1.000000
0.420000
0.000000
0.000000
0.000000
25%
223.500000
0.000000
2.000000
20.125000
0.000000
0.000000
7.910400
50%
446.000000
0.000000
3.000000
28.000000
0.000000
0.000000
14.454200
75%
668.500000
1.000000
3.000000
38.000000
1.000000
0.000000
31.000000
max
891.000000
1.000000
3.000000
80.000000
8.000000
6.000000
512.329200




data["Age"]=data["Age"].fillna(data["Age"].median())
data.describe()


PassengerId
Survived
Pclass
Age
SibSp
Parch
Fare


count
891.000000
891.000000
891.000000
891.000000
891.000000
891.000000
891.000000
mean
446.000000
0.383838
2.308642
29.361582
0.523008
0.381594
32.204208
std
257.353842
0.486592
0.836071
13.019697
1.102743
0.806057
49.693429
min
1.000000
0.000000
1.000000
0.420000
0.000000
0.000000
0.000000
25%
223.500000
0.000000
2.000000
22.000000
0.000000
0.000000
7.910400
50%
446.000000
0.000000
3.000000
28.000000
0.000000
0.000000
14.454200

The count variable shows that 177 values are missing in the Age column.
One solution is to fill in the null values with the median age. We could also impute with the mean age but the median is more robust to outliers.


Q- my first question is what is the difference bewewen the data description one before the medium replacement and after it

The key difference between the data descriptions before and after replacing missing ages with the median lies in the statistics for the Age column. Let's break down the changes:
count: Before imputation, the count for Age was 714, indicating 177 missing values (891 total passengers - 714 recorded ages). After imputation, the count becomes 891, as all missing values are now filled with the median age.
mean: The mean age shifts slightly from 29.699 to 29.362. This change occurs because the missing values, which previously weren't factored into the mean calculation, are now replaced with the median age. The shift reflects the influence of the imputed values on the overall average.
std: The standard deviation (std) decreases from 14.526 to 13.020. This is a significant change. Imputing missing values with the median reduces the spread or variability of the age data. Since the median is a central value, using it to replace missing data tends to cluster the values more closely around the center, thus lowering the standard deviation.
min, 25%, 50%, 75%, max: The minimum, maximum, and quartiles may or may not change depending on the distribution of the data and the median value. In this specific case:
The min remains unchanged at 0.42. This suggests that the lowest age value wasn't missing.
The 25th percentile (first quartile) changes from 20.125 to 22. This means that with the median imputation, a larger proportion of passengers are now 22 or older compared to the original data with missing values.
The median (50th percentile) remains at 28. This is expected since you used the median to fill in missing values.
The changes in the 75th percentile and the max are relatively small, suggesting that fewer older passengers had missing age values.


In essence, imputing with the median makes the Age data more complete but also potentially less representative of the true distribution, particularly affecting the mean and standard deviation. While it addresses the missing data problem, it also introduces a potential bias by artificially reducing the age variability.

Q- fix the issue here 

“"""
Visualize survival based on the gender
"""
data["Died"] = 1 - data["Survived"]


data.groupby('Sex').agg('sum')[['Survived', 'Died']].plot(kind='bar', figsize=(25, 7),
                                                          stacked=True, colors=['g', 'r'])
“ 
{
	"name": "AttributeError",
	"message": "Rectangle.set() got an unexpected keyword argument 'colors'",
	"stack": "---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[14], line 6
      1 \"\"\"
      2 Visualize survival based on the gender 
      3 \"\"\"
      4 data[\"Died\"] = 1 - data[\"Survived\"]
----> 6 data.groupby('Sex').agg('sum')[['Survived', 'Died']].plot(kind='bar', figsize=(25, 7),
      7                                                           stacked=True, colors=['g', 'r'])

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1030, in PlotAccessor.__call__(self, *args, **kwargs)
   1027             label_name = label_kw or data.columns
   1028             data.columns = label_name
-> 1030 return plot_backend.plot(data, kind=kind, **kwargs)

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py:71, in plot(data, kind, **kwargs)
     69         kwargs[\"ax\"] = getattr(ax, \"left_ax\", ax)
     70 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
---> 71 plot_obj.generate()
     72 plot_obj.draw()
     73 return plot_obj.result

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:501, in MPLPlot.generate(self)
    499 self._compute_plot_data()
    500 fig = self.fig
--> 501 self._make_plot(fig)
    502 self._add_table()
    503 self._make_legend()

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1940, in BarPlot._make_plot(self, fig)
   1938 start = np.where(mask, pos_prior, neg_prior) + self._start_base
   1939 w = self.bar_width / 2
-> 1940 rect = self._plot(
   1941     ax,
   1942     self.ax_pos + w,
   1943     y,
   1944     self.bar_width,
   1945     start=start,
   1946     label=label,
   1947     log=self.log,
   1948     **kwds,
   1949 )
   1950 pos_prior = pos_prior + np.where(mask, y, 0)
   1951 neg_prior = neg_prior + np.where(mask, 0, y)

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1884, in BarPlot._plot(cls, ax, x, y, w, start, log, **kwds)
   1873 @classmethod
   1874 def _plot(  # type: ignore[override]
   1875     cls,
   (...)
   1882     **kwds,
   1883 ):
-> 1884     return ax.bar(x, y, w, bottom=start, log=log, **kwds)

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\matplotlib\\__init__.py:1473, in _preprocess_data.<locals>.inner(ax, data, *args, **kwargs)
   1470 @functools.wraps(func)
   1471 def inner(ax, *args, data=None, **kwargs):
   1472     if data is None:
-> 1473         return func(
   1474             ax,
   1475             *map(sanitize_sequence, args),
   1476             **{k: sanitize_sequence(v) for k, v in kwargs.items()})
   1478     bound = new_sig.bind(ax, *args, **kwargs)
   1479     auto_label = (bound.arguments.get(label_namer)
   1480                   or bound.kwargs.get(label_namer))

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:2591, in Axes.bar(self, x, height, width, bottom, align, **kwargs)
   2582 for l, b, w, h, c, e, lw, htch, lbl in args:
   2583     r = mpatches.Rectangle(
   2584         xy=(l, b), width=w, height=h,
   2585         facecolor=c,
   (...)
   2589         hatch=htch,
   2590         )
-> 2591     r._internal_update(kwargs)
   2592     r.get_path()._interpolation_steps = 100
   2593     if orientation == 'vertical':

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\matplotlib\\artist.py:1216, in Artist._internal_update(self, kwargs)
   1209 def _internal_update(self, kwargs):
   1210     \"\"\"
   1211     Update artist properties without prenormalizing them, but generating
   1212     errors as if calling `set`.
   1213 
   1214     The lack of prenormalization is to maintain backcompatibility.
   1215     \"\"\"
-> 1216     return self._update_props(
   1217         kwargs, \"{cls.__name__}.set() got an unexpected keyword argument \"
   1218         \"{prop_name!r}\")

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\matplotlib\\artist.py:1190, in Artist._update_props(self, props, errfmt)
   1188             func = getattr(self, f\"set_{k}\", None)
   1189             if not callable(func):
-> 1190                 raise AttributeError(
   1191                     errfmt.format(cls=type(self), prop_name=k))
   1192             ret.append(func(v))
   1193 if ret:

AttributeError: Rectangle.set() got an unexpected keyword argument 'colors'"
}

Q- it become like his 

“"""
Visualize survival based on the gender
"""
data["Died"] = 1 - data["Survived"]


data.groupby("Sex").agg("sum")[['Survived','Died']].plot(kind='bar' , figsize=(25,7),stacked=True , color=['g','r'])


“ 

Inperpration of this 

This image shows a stacked bar chart comparing survival rates between males and females, likely from some kind of study or dataset. The x-axis represents sex (female and male), while the y-axis shows the count of individuals up to 600.

For females:
- The green bar (representing survivors) is larger, reaching about 230-240 individuals.
- The red bar (representing those who died) is smaller, reaching about 80-90 individuals.

For males:
- The green bar (survivors) is much smaller, reaching about 110-120 individuals.
- The red bar (deaths) is significantly larger, reaching about 460-470 individuals.

Key observations:
1. There are more males than females in the dataset overall.
2. Females have a much higher survival rate compared to males.
3. Males have a dramatically higher death rate compared to females.

This chart suggests a significant disparity in survival rates between males and females in whatever context this data represents. Without more information, it's not possible to determine the specific circumstances or reasons for this disparity, but the difference is quite striking.

It looks like male passengers are more likely to succumb.
Let's plot the same graph but with ratio instead.

Q- give me the code to do the ration so ?

Certainly! To visualize the survival rates as ratios instead of absolute counts, you can normalize the counts by the total number of passengers in each gender group. This way, you'll get the proportion of survivors and deaths for each gender.

Here's how you can do it:

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Assuming data is already loaded
data["Died"] = 1 - data["Survived"]

# Calculate the ratios
survival_ratio = data.groupby("Sex")["Survived"].mean()
death_ratio = data.groupby("Sex")["Died"].mean()

# Plotting the ratios
fig, ax = plt.subplots(figsize=(10, 5))

survival_ratio.plot(kind='bar', ax=ax, color='g', position=0, width=0.4, label='Survived')
death_ratio.plot(kind='bar', ax=ax, color='r', position=1, width=0.4, label='Died')

ax.set_title('Survival and Death Ratios by Gender')
ax.set_xlabel('Sex')
ax.set_ylabel('Ratio')
ax.legend()

plt.show()
```

### Explanation:
1. **Calculate Ratios**: We calculate the mean of the `Survived` and `Died` columns grouped by `Sex`. This gives us the proportion of survivors and deaths for each gender.
2. **Plotting**:
   - We create a bar plot for the survival ratios in green.
   - We create another bar plot for the death ratios in red, placing it side-by-side with the survival ratios.
   - We set the title, labels, and legend for better readability.

This will give you a clear visual representation of the survival and death ratios for each gender.

Q- part Ii

II - Feature engineering
In the previous part, we flirted with the data and spotted some interesting correlations.
In this part, we'll see how to process and transform these variables in such a way the data becomes manageable by a machine learning algorithm.
We'll also create, or "engineer" additional features that will be useful in building the model.
We'll see along the way how to process text variables like the passenger names and integrate this information in our model.
We will break our code in separate functions for more clarity.
But first, let's define a print function that asserts whether or not a feature has been processed.
Data Preprocessing and Feature Engineering for Titanic Survival Prediction
This document outlines the data preprocessing and feature engineering steps taken to prepare the Titanic dataset for a machine learning model. The goal is to transform raw data into a format suitable for algorithm training and improve model performance.
1. Combining Train and Test Data:
The training and test datasets are combined initially to ensure consistent feature engineering across both. The Survived column (target variable) is extracted from the training set and stored separately. The PassengerId column is removed as it's not informative for prediction.
     def get_combined_data():
    train = pd.read_csv('./data/train.csv')
    test = pd.read_csv('./data/test.csv')

    targets = train.Survived
    train.drop(['Survived'], axis=1, inplace=True)

    combined = train.append(test)
    combined.reset_index(inplace=True)
    combined.drop(['index', 'PassengerId'], inplace=True, axis=1)
    
    return combined
   
content_copy Use code with caution.Python
2. Feature Engineering - Extracting Passenger Titles:
Passenger names are parsed to extract titles (e.g., Mr., Mrs., Miss, Master). These titles potentially encode social status information. A dictionary maps extracted titles to broader categories (Officer, Royalty, Mr, Mrs, Miss, Master).
     Title_Dictionary = {
    # ... (Dictionary mapping titles)
}

def get_titles():
    combined['Title'] = combined['Name'].map(lambda name: name.split(',')[1].split('.')[0].strip())
    combined['Title'] = combined.Title.map(Title_Dictionary)
    return combined
   
content_copy Use code with caution.Python
3. Handling Missing Ages:
Missing age values are imputed based on the median age for groups defined by Sex, Pclass (passenger class), and Title. This approach avoids using a single median for all passengers and considers group-specific age distributions. Imputation is performed separately for the training and test sets (using training data statistics for both) to prevent data leakage.
     def fill_age(row):
    condition = (
        (grouped_median_train['Sex'] == row['Sex']) & 
        (grouped_median_train['Title'] == row['Title']) & 
        (grouped_median_train['Pclass'] == row['Pclass'])
    ) 
    return grouped_median_train[condition]['Age'].values[0]

def process_age():
    combined['Age'] = combined.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)
    return combined
   
content_copy Use code with caution.Python
4. Remaining Missing Values:
The code identifies missing values in Fare, Embarked, and Cabin features, noting that these will be addressed later.
This structured explanation provides a clearer and more organized overview of the provided code and its purpose. It separates the code blocks and explains each step's logic, making the process easier to understand. It also emphasizes important aspects like preventing data leakage.
Q- how to solve this error 
“combined=process_age()
“
{
	"name": "TypeError",
	"message": "'numpy.ndarray' object is not callable",
	"stack": "---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[46], line 1
----> 1 combined=process_age()

Cell In[45], line 10, in process_age()
      8 def process_age():
      9     global combined
---> 10     combined['Age'] = combined.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)
     11     status('age')
     12     return combined

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:10374, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)
  10360 from pandas.core.apply import frame_apply
  10362 op = frame_apply(
  10363     self,
  10364     func=func,
   (...)
  10372     kwargs=kwargs,
  10373 )
> 10374 return op.apply().__finalize__(self, method=\"apply\")

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\pandas\\core\\apply.py:916, in FrameApply.apply(self)
    913 elif self.raw:
    914     return self.apply_raw(engine=self.engine, engine_kwargs=self.engine_kwargs)
--> 916 return self.apply_standard()

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\pandas\\core\\apply.py:1063, in FrameApply.apply_standard(self)
   1061 def apply_standard(self):
   1062     if self.engine == \"python\":
-> 1063         results, res_index = self.apply_series_generator()
   1064     else:
   1065         results, res_index = self.apply_series_numba()

File c:\\Users\\PCS\\Desktop\\mlops\\Titanic\\venv\\Lib\\site-packages\\pandas\\core\\apply.py:1081, in FrameApply.apply_series_generator(self)
   1078 with option_context(\"mode.chained_assignment\", None):
   1079     for i, v in enumerate(series_gen):
   1080         # ignore SettingWithCopy here in case the user mutates
-> 1081         results[i] = self.func(v, *self.args, **self.kwargs)
   1082         if isinstance(results[i], ABCSeries):
   1083             # If we have a view on v, we need to make a copy because
   1084             #  series_generator will swap out the underlying data
   1085             results[i] = results[i].copy(deep=False)

Cell In[45], line 10, in process_age.<locals>.<lambda>(row)
      8 def process_age():
      9     global combined
---> 10     combined['Age'] = combined.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)
     11     status('age')
     12     return combined

Cell In[45], line 7, in fill_age(row)
      1 def fill_age(row):
      2     condition= (
      3         (grouped_median_train['Sex']==row['Sex']) &
      4         (grouped_median_train['Title']==row['Title']) &
      5         (grouped_median_train['Pclass']==row['Pclass'])
      6     )
----> 7     return grouped_median_train[condition]['Age'].values(0)

TypeError: 'numpy.ndarray' object is not callable"
}

# Grouped median for age imputation
grouped_median_train = combined.groupby(['Sex', 'Pclass', 'Title'])['Age'].median().reset_index()

def fill_age(row):
    condition = (
        (grouped_median_train['Sex'] == row['Sex']) &
        (grouped_median_train['Title'] == row['Title']) &
        (grouped_median_train['Pclass'] == row['Pclass'])
    )
    return grouped_median_train[condition]['Age'].values[0]

def process_age():
    combined['Age'] = combined.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)
    return combined

combined = process_age()

Q- solve this now 

“Processing Fare
Let's imputed the missing fare value by the average fare computed on the train set
In [37]:
def process_fares():
    global combined
    # there's one missing fare value - replacing it with the mean.
    combined.Fare.fillna(combined.iloc[:891].Fare.mean(), inplace=True)
    status('fare')
    return combined


This function simply replaces one missing Fare value by the mean.
In [38]:
combined = process_fares()
“ 
Processing fare : ok
C:\Users\PCS\AppData\Local\Temp\ipykernel_17704\813712845.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  combined.Fare.fillna(combined.iloc[:891].Fare.mean(), inplace=True)