How to get the survival duration prediction for each individual in the data by using the Kaplan-Meier method?
The Kaplan Meier curve is a summary statistic, similar to the average. Therefore, it is an unconditional statistic. If you are interested in the conditional survival, we can use the Kaplan Meier curve to do that too. Some notation first. Let $T$ be the unknown time of death, and $S(t) = P(T &gt; t)$ the unconditional survival curve. 

Given a subject has lived past some time $s$, we'd like to know $P(T &gt; t | T &gt; s)$. We can expand this conditional probability: 

$$ 
\begin{align}P(T &gt; t | T &gt; s) &amp;= \frac{ P(T &gt; t \text{ and } T &gt; s)}{P(T &gt; s)}\\
&amp;= \frac{ P(T &gt; t)}{P(T &gt; s)} \text{   ,since t &gt; s}\\
&amp;= \frac{ S(t)}{S(s)}
\end{align}
$$

That is, we scale that entire survival curve by $S(s)$. 

In practice, if you have a censored subject and you'd like to know their conditional survival after, you just scale by the survival function at their censored time. 

In lifelines, there is the property KaplanMeierFitter.conditional_time_to_event_ which computes the median remaining survival time given survival up to time $t$. This can be used as a conditional prediction. 

kmf = KaplanMeierFitter().fit(t, e)
censored_times = t[~e.astype(bool)]
predicted_life_remaining = kmf.conditional_time_to_event_.asof(censored_times)